On NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10/DGX Spark) a larger
physical batch (n_ubatch) materially lifts MoE prefill throughput - measured on
a GB10 with Qwen3-30B-A3B to lift the prefill ceiling and saturate at ~2048.
When a model config leaves `batch:` unset, EffectiveBatchSize now picks 2048 on
Blackwell instead of 512; explicit `batch:` always overrides. Detection is a
shared, cached Go helper (xsysinfo.IsNVIDIABlackwell, nvidia-smi compute_cap
>= 12). Logic is isolated in core/backend/hardware_defaults.go and applied at
the common ModelOptions builder, so it covers the C++ llama.cpp backend too.
Measured (GB10, Qwen3-Coder-30B-A3B MXFP4): prefill ub512 2994 -> ub2048 3316
t/s; saturates past 2048. Also recorded in the DGX gap plan: 4-bit quant alone
captures the decode win (Q4_K_M 93.5 >= MXFP4 86.4 t/s), MXFP4's only edge is
prefill via Blackwell FP4 tensor cores.
Tests: hardware_defaults_internal_test.go; existing NBatch specs pinned to the
no-Blackwell branch for determinism.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Captures the full dgx.casa investigation: Q8/F16/vLLM baselines, concurrency
sweeps, paged-patch (no concurrency effect), nsys+code root-cause (MoE int8
MMQ on Ampere-class tensor cores = 74.5% compute, no FP8 path), and the
lever plan.
Measured wins:
- Lever 1 (MXFP4 / Blackwell FP4 path): decode +50-66% over Q8, prefill
plateau +66% (2200->3650). MXFP4 decode beats vLLM FP8 at B=1 (83 vs 48),
near-parity B=8. Prefill still plateaus (fused-MoE-GEMM gap).
- Lever 2 (ubatch): saturates at 2048; ceiling is the kernel, not batch.
Designed (not built): Lever 3 fused FP4/FP8 MoE grouped GEMM, Lever 4 FP8
GEMM (needs ggml_mul_mat_ext scale plumbing), Lever 5 tcgen05 kernels, and
the complete paged attention (on-demand alloc + gather-read + continuous
batching + prefix sharing). Honest scope: each is multi-week kernel/systems
work.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Wire paged, non-contiguous fixed-size BLOCK placement into the real
llama.cpp KV cache (find_slot), behind env LLAMA_KV_PAGED, and validate
Gate 0 on a real GGUF: Qwen3-0.6B greedy generation is TOKEN-IDENTICAL to
the contiguous cache while its KV is physically scattered across permuted
blocks (cells 0-15, 144-159, 32-47, ...). Proven non-contiguous via
LLAMA_KV_PAGED_DEBUG, not a silent fallback.
This retires the correctness premise of paged attention IN THE MODEL (not
just at the ggml-op level): attention is invariant to physical KV placement,
because reads use per-cell pos/seq metadata for masking. The patch lives at
patches/0001-paged-kv-block-placement.patch (against llama.cpp 0253fb21f).
Scope: storage/placement layer, single sequence. Remaining (P4): the
gather-read compute path (attend only a seq's own blocks) for the throughput
win, and the multi-sequence driver. README updated with repro + status.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Capture verified state (P0 manager parity, P1 ggml write/gather, P2 attention
numerics 7.5e-08, P3 capacity 9.2x + prefix-sharing 11.3x) and the exact
remaining work: wire build_attn_paged into llama-graph.cpp and validate
token-identical generation on Qwen3-0.6B (Gate 0), then win-2 throughput.
Records the integration seams (create_memory, find_slot, get_k/get_v,
build_attn, mask) and the honest caveats (unified cache already shares a
pool; vLLM's classic kernel is deprecated) so the next session starts warm.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Quantify the two multi-tenant wins that are properties of the host-side
block model (vLLM-parity), independent of the in-model compute path:
WIN 1 concurrency capacity @ 512-block budget
contiguous (reserve n_ctx/seq): 4 sequences
paged (on-demand blocks): 37 sequences
--> 9.2x more concurrent sequences
WIN 3 cross-tenant prefix sharing (32 tenants, 1024-tok shared prefix)
prefix-cache OFF: 2176 physical blocks
prefix-cache ON: 192 physical blocks
--> 11.3x less KV memory
WIN 2 (throughput) is deliberately reported as PENDING: it requires the
paged gather-read path wired into llama-graph.cpp (Gate 0) and is not
measurable at the allocation layer. The win-1 baseline is per-sequence
n_ctx reservation (stream mode); llama.cpp's unified cache already shares
one pool, so the honest win there is on-demand sizing + prefix dedup.
Phase 3 (partial) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Retire the central numeric risk from the design: feeding gather-to-scratch
KV (a sequence whose blocks are non-contiguous in the shared pool, [2,1,5])
into ggml's standard attention ops produces correct attention.
Path under test: set_rows write -> get_rows gather (K and V) ->
mul_mat(K,Q) -> soft_max_ext -> mul_mat(V^T, probs). Result is compared
against an independent host-computed softmax attention over the same K/V/Q.
Max abs error ~7.5e-08 (n_kv=48, d=8, n_q=4).
This proves the paged read path is numerically sound on CPU with no new
ggml op. Remaining: wire build_attn_paged into llama-graph.cpp and validate
Gate 0 (token-identical greedy generation in a real model).
Phase 2 (core) of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Validate the paged KV read/write path at the ggml-op level, driven by
PagedKVManager:
- write: ggml_set_rows(pool, k_src, slot_mapping) scatter K rows by slot
- read: ggml_get_rows(pool, gather_idx) gather a seq's slots into
contiguous scratch (the tensor an attention kernel consumes)
The test forces a non-contiguous, out-of-order physical block layout
(allocate seqA+seqB, free seqA, reallocate seqC -> blocks [2,1,5]) and
proves gather(write(x)) == x plus cross-sequence isolation in the shared
pool. This de-risks the central question (does slot-addressed paged storage
round-trip correctly through ggml) before the llama-graph integration.
Pool is statically allocated via ggml_backend_alloc_ctx_tensors, mirroring
how llama.cpp allocates its KV cache. CPU backend, no new ggml op.
Built against ggml from the vendored llama.cpp checkout.
Phase 1 of docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(config): add chat_template_kwargs model field + resolver
Adds the ChatTemplateKwargs model-config map and RequestMetadata carrier,
plus ResolveChatTemplateKwargs which layers the config map under coerced
request metadata. Foundation for generic jinja chat-template kwargs (issue #10329).
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backend): forward resolved chat_template_kwargs blob to backends
gRPCPredictOpts now merges per-request client metadata over the server-derived
enable_thinking/reasoning_effort (reaching all backends via the standalone keys)
and serialises the resolved chat_template_kwargs map into a JSON blob for
llama.cpp, written last so a client cannot clobber it. Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(http): wire request metadata to config.RequestMetadata
The OpenAI request metadata field was parsed but unused; stamp it onto the
per-request ModelConfig so gRPCPredictOpts forwards it as chat_template_kwargs
overrides. Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): generic chat_template_kwargs merge (drop per-key blocks)
Replace the per-key enable_thinking/reasoning_effort handling in both the
streaming and non-streaming chat paths with a single block that parses the
chat_template_kwargs JSON blob resolved by the Go layer and merges every key
into body_json. New jinja template levers (e.g. preserve_thinking) now need
no C++ change. Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: document custom chat_template_kwargs (model + per-request)
Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(backend): pin reasoning_effort as a string in the chat_template_kwargs blob
Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(http): e2e guard pinning chat_template_kwargs forwarded to gRPC
Adds an ECHO_PREDICT_METADATA marker to the mock-backend that echoes the
received PredictOptions.Metadata, and an app_test.go spec that drives a real
/v1/chat/completions request (model chat_template_kwargs + per-request metadata
override) and asserts the exact metadata + chat_template_kwargs blob the REST
layer forwards to gRPC. Locks the REST->gRPC contract against regressions. Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(config): grandfather chat_template_kwargs in registry coverage
chat_template_kwargs is a free-form map[string]any (like engine_args, already
on the list), not a scalar the config UI registry can surface, so it is exempt
from the registry-entry requirement. Fixes the TestAllFieldsHaveRegistryEntries
failure introduced by the new field. Issue #10329.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(router): score classifier production-readiness
Conversation trimming runs through the classifier model's chat template
and trims by exact token count, sized to the model's n_batch which is
now scaled to context so long probes can't crash the backend. Missing
chat_message templates are a hard error at router build time. Router-
facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve
ModelConfig per call so a model installed post-startup doesn't bind a
stub Backend="" config and silently fall into the loader's auto-
iterate path.
New 'vector_store' backend trace recorded inside localVectorStore on
every Search/Insert — including the backend-load-failure path that
previously vanished into an xlog.Warn — with outcome tagging
(hit/miss/empty_store/backend_load_error/find_error/insert_error/ok).
Companion cleanup drops misleading similarity:0 and input_tokens_count:0
from non-hit and text-mode traces.
Gallery local-store-development aliases to 'local-store' so the master
image satisfies pkg/model.LocalStoreBackend lookups from the embedding
cache.
Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key
(the original bug); ModelTokenize nil-guard; non-fatal mitm proxy
startup; PII 'route_local' renamed to 'allow' with docs/UI in sync;
model-editor footer no longer eats the edit area on small screens;
several config-editor template/dropdown/section fixes.
Tests: e2e router specs (casual/code-hint + long-conversation trim),
vector_store trace specs, lazy-factory specs, gallery dev-alias
resolution, Playwright trace badge + scroll regression.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(backend): auto-size batch to context for embedding and rerank models
Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins.
Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse.
Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch.
Assisted-by: claude-code:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(gallery): raise arch-router scoring output cap via parallel:64
Scoring decodes the whole prompt+candidate in a single llama_decode and
reads one logit row per candidate token. The vendored llama.cpp server
caps causal output rows at n_parallel, so the default of 1 aborts with
GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route
labels. Set options: [parallel:64] on both arch-router quant entries to
lift the cap; kv_unified (the grpc-server default) keeps the full context
per sequence, so this does not split the KV cache.
Assisted-by: claude-code:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(llama-cpp): bump to 8f83d6c for mtmd video input support
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): forward video input to mtmd (template + non-template paths)
Wire request->videos() into grpc-server.cpp mirroring the existing image
and audio handling: a video_data build + non-template files extraction, and
input_video chat chunks on the tokenizer-template path. allow_video is
auto-set at model load by the vendored upstream chat_params.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): add video attachment support to the chat UI
Mirror the image/audio attachment path for video: emit video_url content
parts, accept video/* in the picker, keep video files as base64, show a
film icon badge, and render attached video inline with a <video> player.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama-cpp): patch mtmd video stdin double-close (heap crash)
Upstream mtmd video input (ggml-org/llama.cpp#24269) double-fcloses the
ffmpeg/ffprobe stdin FILE: feed_stdin() fclose()s the FILE returned by
subprocess_stdin() (which is sp->stdin_file), then subprocess_destroy()
fclose()s the same pointer again -> heap corruption that aborts the
backend on any base64 input_video request (the CLI --video file path is
unaffected). Vendor a one-line fix (null sp->stdin_file after fclose)
via prepare.sh's patches/ until upstream merges it.
Verified e2e with gemma-4-e2b-it-qat-q4_0: video frames decode via
ffmpeg and the model answers correctly (red clip -> 'Red', blue -> 'Blue').
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(llama-cpp): re-pin to upstream #24316, drop vendored stdin patch
Upstream replaced the ad-hoc video stdin handling with a proper RAII
refactor (ggml-org/llama.cpp#24316, "mtmd: refactor video subproc
handling"), which includes the same `sp->stdin_file = nullptr` guard our
patch added (plus join-before-destroy ordering). Re-pin LLAMA_VERSION to
that branch head and drop patches/0001 - it's now redundant.
Verified e2e with gemma-4-e2b-it-qat-q4_0: no crash, video frames decode
and the model answers correctly (red clip -> "Red", blue -> "Blue").
NOTE: #24316 is not yet merged, so this pins to its branch-head commit
(28ca1e60). Re-pin to the squash-merge commit on master once it lands,
otherwise `git fetch` may lose the commit after the branch is deleted.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* chore(turboquant): bump TheTom/llama-cpp-turboquant to 7d9715f1
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(turboquant): drop obsolete legacy-spec shim after fork rebased
The TheTom/llama-cpp-turboquant fork (pin c9aa86a) rebased past the
upstream common_params_speculative refactor (ggml-org/llama.cpp
#22397/#22838/#22964), the model_tgt rename (#22838) and get_media_marker
(#21962). The old fork-compat shim forced now-wrong legacy code paths,
breaking the build with errors like 'struct common_params_speculative has
no member named mparams_dft / type' and 'server_context_impl has no member
named model'.
Remove the obsolete LOCALAI_LEGACY_LLAMA_CPP_SPEC branches from the shared
grpc-server.cpp (stock llama-cpp and the modern fork both take the modern
path now), and narrow the one remaining gap (the fork still lacks
common_params::checkpoint_min_step) to a dedicated
LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP guard injected by
patch-grpc-server.sh. The patch script now only adds the turbo2/3/4
KV-cache types and injects that one macro.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(turboquant): HIP-port the fork's CUDA additions (copy2d 3D-peer + cudaEventCreate)
The turboquant fork adds/modifies a few ggml-cuda.cu spots with CUDA APIs that
ggml's HIP/MUSA shim does not provide, breaking the -gpu-rocm-hipblas-turboquant
build. patches/0001-hip-guard-copy2d-peer-fastpath.patch (applied by
apply-patches.sh) ports them:
- Guard ggml_cuda_copy2d_across_devices's 3D-peer copy fast path with
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) so HIP/MUSA fall through
to the existing cudaMemcpyAsync staging fallback (HIP genuinely lacks
cudaMemcpy3DPeerAsync, per the fork's own comment).
- Create the device event in ggml_backend_cuda_device_event_new with the
HIP-aliased cudaEventCreateWithFlags(.., cudaEventDisableTiming) instead of the
un-aliased plain cudaEventCreate, matching this file's own usage elsewhere.
CUDA builds are unaffected.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* ci(turboquant): drop the ROCm/hipblas build flavor
The TheTom/llama-cpp-turboquant fork is not ROCm-clean at the current pin:
beyond the CUDA-API gaps already patched (3D-peer copy, cudaEventCreate),
its llama.cpp base fails to compile the flash-attention MMA f16 kernels for
head-dim 640 under HIP (cols_per_warp evaluates to 0 -> division-by-zero /
non-constant static asserts in fattn-mma-f16.cuh). That is a deep
ggml-on-ROCm kernel issue, not something a small fork patch can paper over.
Drop -gpu-rocm-hipblas-turboquant from the build matrix so turboquant still
ships for cpu / cublas / vulkan / sycl. Re-add it once the fork's HIP path
compiles (or upstream ggml fixes the large-head-dim MMA kernels for ROCm).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat: forward reasoning_effort to the backend so jinja models honor it
reasoning_effort was only mapped to the binary enable_thinking toggle and
otherwise reached Go-side templates — it was never sent to the backend. So
jinja-templated models whose chat template keys on reasoning_effort (gpt-oss
Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and
kept emitting <think>.
Forward the effective reasoning_effort to the backend as a chat_template_kwarg
(mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions
metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort
and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort
(request value overrides config default, none->disable / level->enable, an
operator's reasoning.disable wins). request.go now uses that helper.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): set the pipeline LLM's reasoning_effort
Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime
model is built (per-session copy, overrides the LLM's own reasoning_effort),
and surface the resolved effort on the template input so Go-templated models
get it too. jinja models receive it via the backend metadata. This lets a
realtime pipeline disable thinking on models that only honor reasoning_effort
(e.g. LFM2.5), which enable_thinking can't.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): skip begin-of-stream null partial in PredictStream
Upstream llama.cpp (ggml-org/llama.cpp#23884), pulled in by this bump,
now emits an initial "begin" partial whose to_json() returns null. It
exists only to signal the HTTP layer to flush 200 status headers before
any token is produced.
gRPC has no such concept, and PredictStream had no guard: the null result
was fed straight into build_reply_from_json, which threw an uncaught
exception. That surfaced as a generic "Unexpected error in RPC handling"
and the task was cancelled the instant it launched, breaking the
PredictStream e2e spec.
Skip null results in both the first-result handling and the streaming
loop, mirroring upstream's own `if (first_result_json == nullptr)` guard.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
fix(turboquant): guard upstream-only grpc-server fields for fork build
backend/cpp/llama-cpp/grpc-server.cpp is reused by the turboquant build,
which compiles against an older llama.cpp fork (TheTom/llama-cpp-turboquant).
Two recent changes added references to upstream-only struct fields outside the
existing LOCALAI_LEGACY_LLAMA_CPP_SPEC guards:
- common_params::checkpoint_min_step (default + option handler), added with
the ggml-org/llama.cpp 35c9b1f3 bump (#9998)
- the common_params_speculative::draft tensor_buft_overrides sentinel
termination (#9919), which sat after the guard's #endif
The fork has neither field, so grpc-server.cpp failed to compile for every
turboquant flavor. Wrap the three references in #ifndef
LOCALAI_LEGACY_LLAMA_CPP_SPEC, matching the existing fork-compat guards, so the
stock llama-cpp build is unchanged and the fork build skips them. Update
patch-grpc-server.sh's doc comment to record what the macro now gates out.
Verified by a local fallback-flavor turboquant build: grpc-server.cpp compiles
against the fork and the backend image builds.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): track upstream rename checkpoint_every_nt -> checkpoint_min_step
Upstream llama.cpp renamed common_params::checkpoint_every_nt to
checkpoint_min_step and changed its default from 8192 to 256. The semantics
also shifted: it used to enforce a fixed checkpoint cadence during prefill,
now it sets a minimum spacing between context checkpoints. Track the new
field name in grpc-server.cpp and accept the old option names as backward-
compatible aliases for users with existing configs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Add a routing middleware stack and a cloud-proxy backend.
* cloud-proxy: a Go gRPC backend that forwards OpenAI- and
Anthropic-shaped chat requests to upstream providers, with an
optional translate mode (OpenAI request -> Anthropic /v1/messages
-> OpenAI response) and full tool-calling support.
* routing: admission control, content-aware model routing
(embedding cache + classifier + rerank + Arch-Router score),
PII detection/redaction (regex + NER) with streaming filter and
OpenAI/Anthropic adapters, and a per-user/per-key billing recorder
backed by GORM or in-memory storage.
* middleware: UsageMiddleware records usage via the billing recorder,
plus admission, route-model, usage-stamp and trace middlewares.
* observability: BackendTrace ring buffer stores full request bodies
(capped), MITM proxy emits structured trace events, and router
classifier decisions surface at /api/router/decide.
* gallery: Arch-Router-1.5B (Q4_K_M and Q8_0).
* UI: cloud-proxy model-editor fields, classifier system-prompt and
score-normalization config, and a Traces page rendering request
bodies.
Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Aligns LocalAI's llama-cpp gRPC backend with upstream's auto-on prompt
cache path so repeated system prompts (agents, OpenAI/Anthropic-compatible
CLIs, coding assistants) skip prefill on subsequent calls without any
YAML changes. Reported in #9921.
Upstream's server enables `kv_unified=true` (and bumps `n_parallel` to 4)
when slot count is auto, which unlocks `cache_idle_slots`. LocalAI
hardcodes `n_parallel=1` and so far also hardcoded `kv_unified=false`,
which silently force-disables idle-slot saving at server init. The host
prompt cache was allocated but never written across requests.
Changes in backend/cpp/llama-cpp/grpc-server.cpp:
- params.kv_unified: false -> true (single-slot path now benefits from
the prompt cache; users can opt out with `kv_unified:false`)
- params.n_ctx_checkpoints: 8 -> 32 (match upstream default)
- params.cache_idle_slots = true initialized explicitly (upstream default)
- params.checkpoint_every_nt = 8192 initialized explicitly (upstream default)
- New option parsers: cache_idle_slots / idle_slots_cache,
checkpoint_every_nt / checkpoint_every_n_tokens
Docs:
- features/text-generation.md: fix misleading `cache_ram` description
(it's the host-side prompt cache, not the KV cache), document the
kv_unified + cache_ram + cache_idle_slots interaction, add rows for
the two newly-exposed options, and add a worked example for the
agent/CLI workload from the issue.
- advanced/model-configuration.md: mark the legacy `prompt_cache_path`
/ `prompt_cache_all` / `prompt_cache_ro` YAML fields as unused by the
llama-cpp gRPC backend (they target upstream's CLI completion tool
and are not consumed by grpc-server.cpp) and point readers at the
new prompt-cache explainer.
Closes#9921
Assisted-by: claude:opus-4.7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
llama.cpp's model loader asserts back().pattern == nullptr on
params.tensor_buft_overrides (and on params.kv_overrides.back().key[0]
== 0) before binding them into llama_model_params. PR #8560 attempted
to satisfy llama_params_fit's placeholder requirement by pre-filling
params.tensor_buft_overrides up to llama_max_tensor_buft_overrides()
*before* the option-parse loop. Any subsequent push_back from
override_tensor / draft_cpu_moe / draft_n_cpu_moe / draft_override_tensor
then appended real entries after the placeholders, leaving back() with
a real pattern and tripping the assert. The draft override vector
likewise had no terminator at all.
Mirror upstream common/arg.cpp:645-658 instead: real entries are
pushed during option parsing, and after parsing we pad the main vector
up to ntbo (placeholders land at the end, so back() is always nullptr)
and append a single {nullptr, nullptr} to the draft vector when it is
non-empty. The existing kv_overrides terminator block already matches
upstream and stays.
Verified against ggml-org/llama.cpp@5cbaa5e: only tensor_buft_overrides
(main + draft) and kv_overrides are sentinel-terminated common_params
fields; everything else is size-driven std::vector.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(llama-cpp): bump to MTP-merge SHA and document draft-mtp spec type
Update LLAMA_VERSION to 0253fb21 (post ggml-org/llama.cpp#22673 merge,
2026-05-16) to pick up Multi-Token Prediction support.
No grpc-server.cpp changes are required: the existing `spec_type` option
delegates to upstream's `common_speculative_types_from_names()`, which
already accepts the new `draft-mtp` name. The `n_rs_seq` cparam needed
by MTP is auto-derived inside `common_context_params_to_llama` from
`params.speculative.need_n_rs_seq()`, and when no `draft_model` is set
the upstream server builds the MTP context off the target model itself.
Docs: extend the speculative-decoding section of the model-configuration
guide with the new type, both load paths (MTP head embedded in the main
GGUF vs. separate `mtp-*.gguf` sibling), the PR's recommended
`spec_n_max:2-3`, and the chained `draft-mtp,ngram-mod` recipe. Also
notes that the upstream `-hf` auto-discovery of `mtp-*.gguf` siblings is
not wired through LocalAI's gRPC layer.
Agent guide: short note explaining that new upstream spec types are
picked up automatically and that MTP needs no gRPC plumbing.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): auto-detect MTP heads and enable draft-mtp on import + load
Detect upstream's `<arch>.nextn_predict_layers` GGUF metadata key (set by
`convert_hf_to_gguf.py` for Qwen3.5/3.6 family models and similar) and,
when present and the user has not configured a `spec_type` explicitly,
auto-append the upstream-recommended speculative-decoding tuple:
- spec_type:draft-mtp
- spec_n_max:6
- spec_p_min:0.75
The 0.75 p_min is pinned defensively because upstream marks the current
default with a "change to 0.0f" TODO; locking it here keeps acceptance
thresholds stable across future llama.cpp bumps.
Detection runs in two places:
- The model importer (`POST /models/import-uri`, the `/import-model`
UI) range-fetches the GGUF header for HuggingFace / direct-URL
imports via `gguf.ParseGGUFFileRemote`, with a 30s timeout and
non-fatal error handling. OCI/Ollama URIs are skipped because the
artifact is not directly streamable; the load-time hook covers them
once the file is on disk.
- The llama-cpp load-time hook (`guessGGUFFromFile`) reads the local
header on every model start and appends the same options if
`spec_type` is not already set.
Both paths share `ApplyMTPDefaults` and respect an explicit user-set
`spec_type:` / `speculative_type:` so YAML overrides win. Ginkgo
specs cover the append, preserve-user-choice, legacy alias, and nil
safety paths.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(importer): resolve huggingface:// URIs before MTP header probe
`gguf.ParseGGUFFileRemote` only speaks HTTP(S), but the importer was
handing it the raw `huggingface://...` URI directly (and similarly for
any other custom downloader scheme). Live-test against
`huggingface://ggml-org/Qwen3.6-27B-MTP-GGUF/Qwen3.6-27B-MTP-Q8_0.gguf`
exposed this: the probe failed with `unsupported protocol scheme
"huggingface"`, was caught by the non-fatal error path, and the MTP
options were silently never applied to the generated YAML.
Route every candidate URI through `downloader.URI.ResolveURL()` and
require the resolved form to be HTTP(S). After the fix the probe
successfully reads `<arch>.nextn_predict_layers=1` from the real HF
GGUF and the emitted ConfigFile carries spec_type:draft-mtp,
spec_n_max:6, spec_p_min:0.75 as intended.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The llama.cpp backend already accepts a free-form options: array in the
model config that maps to common_params fields, but a coverage audit
against upstream pin 7f3f843c flagged 12 user-visible knobs that were
neither set via the typed proto fields nor reachable via options:.
Wire them up under the existing if/else chain in params_parse, before
the speculative section. Each new option follows the file's prevailing
patterns (try/catch around numeric parses, the same true/1/yes/on bool
form used elsewhere, hardware_concurrency() fallback for thread counts,
mirror of draft_override_tensor for override_tensor).
Top-level / batching / IO:
- n_ubatch (alias ubatch) -- physical batch size; was previously
force-aliased to n_batch at line 482, blocking embedding/rerank
workloads that need independent control
- threads_batch (alias n_threads_batch) -- main-model batch threads;
mirrors the existing draft_threads_batch
- direct_io (alias use_direct_io) -- O_DIRECT model loads
- verbosity -- llama.cpp log threshold (line 479 had this commented
out)
- override_tensor (alias tensor_buft_overrides) -- per-tensor buffer
overrides for the main model; mirrors draft_override_tensor
Embedding / multimodal:
- pooling_type (alias pooling) -- mean/cls/last/rank/none; previously
only auto-flipped to RANK for rerankers
- embd_normalize (alias embedding_normalize) -- and the embedding
handler now reads params_base.embd_normalize instead of a hardcoded
2 at the previous embd_normalize literal in Embedding()
- mmproj_use_gpu (alias mmproj_offload) -- mmproj on CPU vs GPU
- image_min_tokens / image_max_tokens -- per-image vision token budget
Reasoning surface (the audit-focus three; LocalAI's existing
ReasoningConfig.DisableReasoning only feeds the per-request
chat_template_kwargs.enable_thinking and does not touch any of these):
- reasoning_format -- none/auto/deepseek/deepseek-legacy parser
- enable_reasoning (alias reasoning_budget) -- -1/0/>0 thinking budget
- prefill_assistant -- trailing-assistant-message prefill toggle
All 14 referenced fields exist on both the upstream pin and the
turboquant fork's common.h, so no LOCALAI_LEGACY_LLAMA_CPP_SPEC guard
is needed.
Docs: extend model-configuration.md with new "Reasoning Models",
"Multimodal Backend Options", "Embedding & Reranking Backend Options",
and "Other Backend Tuning Options" subsections; also refresh the
Speculative Type Values table to show the new dash-separated canonical
names alongside the underscore aliases LocalAI still accepts.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): adapt to upstream COMMON_SPECULATIVE_TYPE_DRAFT rename
ggml-org/llama.cpp#22964 ("spec: update CLI arguments for better
consistency") renamed the speculative type enum values:
COMMON_SPECULATIVE_TYPE_DRAFT -> COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE
COMMON_SPECULATIVE_TYPE_EAGLE3 -> COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3
and the registered name strings flipped from underscore- to dash-
separated form (e.g. ngram_simple -> ngram-simple), with the bare
draft/eagle3 aliases replaced by draft-simple/draft-eagle3.
This broke the build with the new LLAMA_VERSION on every variant
(vulkan/arm64, darwin and likely all the rest) at grpc-server.cpp:461.
Update the upstream branch of the speculative-type fallback to use the
new identifier (the LOCALAI_LEGACY_LLAMA_CPP_SPEC fork branch keeps the
old name), and normalize spec_type option tokens before passing them to
common_speculative_types_from_names so existing model configs that say
spec_type:draft / spec_type:ngram_simple keep working.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
