Model-local KV caches¶

Some models keep their attention KV as HuggingFace transformers cache objects instead of using the engine's paged KV manager. Each such model declares what it holds by implementing model_local_kv_specs(), and OmniGPUModelRunner.load_model sums and logs the result after load.

What is and is not already accounted for¶

This distinction decides what the protocol is for, and an earlier revision of this page got it wrong by claiming none of this memory was accounted for.

CUDA-graph capture happens during load_model -- MiMo in its constructor, Qwen3-TTS in load_weights. determine_available_memory() then samples per-process GPU memory through NVML and subtracts it from the requested budget (vllm_omni/worker/base.py). So the graph-resident portion is already charged. Subtracting the declared total from the KV budget would double-count it.

What is not charged is the per-request portion, which is allocated after profiling. That half is not small: Qwen3-TTS's codec decoder is 4.44 MiB per request, so a server at max_num_seqs=256 reaches roughly 1.1 GiB that no per-stage number predicted.

The consumer therefore reports and does not subtract.

What the four models declare¶

No cache is bounded by max_model_len. Each has its own mechanism: a sliding window, a decode-loop trip count, an encoder frame limit, a constant.

ming's geometry does not exist in this repo. self._llm_config is a Qwen2Config from the checkpoint, so layers, kv-heads, head-dim and dtype are only knowable after load. That is what makes this a runtime query rather than a table.

Lifetime is not multiplicity¶

scope records when memory appears and goes. It does not decide how wide the allocation is, and an earlier revision that derived scaling from it was wrong in two different directions at once:

• ming's talker is per-call but serialized -- forward takes runtime_additional_information[0] and runs the whole AR loop inline -- so scaling it by max_num_seqs over-reported it by that factor.
• MiniCPM-o's encoder is per session, not per sequence, so max_num_seqs is simply the wrong number for it.

So width is declared explicitly, by naming the driver. RowDriver has two values, because two are all the known caches need:

• FIXED -- a count the model controls, with rows_fixed and a required rows_reason. rows_fixed=1 on a cache that looks per-request is a claim a reviewer must be able to check.
• MAX_NUM_SEQS -- one row per in-flight sequence; only the engine knows the value.

A third value belongs here when a model actually has a cache the engine widens by some other number. An earlier revision added one speculatively, for a single declarer, and paid for it with a class hierarchy, an engine-capacity object and a conditional-annotation field.

rows counts row-equivalents at peak and deliberately does not distinguish "one allocation N rows wide" from "N allocations of one row". Those cost the same, and a revision that tried to carry both got the object topology wrong in three of four declarers: MiniCPM-o keeps one cache object per session while asserting batch size 1, and both graph pools keep one distinct object per captured bucket. The layout belongs in allocation_note, where it cannot be mistaken for arithmetic.

Declaring nothing is a valid declaration¶

A declaration must be inert when its path is. Qwen3-TTS holds KV only in async-chunk mode -- stateless decoding runs _forward_exact, which calls the transformer without use_cache -- so the stateless path declares nothing. Getting this wrong is easy in both directions, and both have happened here: one revision hung the declaration off the CUDA-graph wrapper and reported zero under enforce_eager, where the eager async-chunk path does allocate; the next declared unconditionally and invented roughly a gigabyte for stateless deployments.

Where the model cannot see the deciding setting, it declares the honest unit and says so in allocation_note. MiniCPM-o's encoder cache exists only on the streaming path and is one object per session; the session cap is not visible from the model, so it declares one session rather than scaling by a number it would have to guess at.

Finding the declarers¶

collect_model_local_kv_specs() walks named_modules(). Two wrinkles:

• The runner's self.model may be a CUDAGraphWrapper, which is not an nn.Module but forwards attribute access to its runnable. The walk therefore works either way, but entering through both the wrapper and its runnable would count a root-level declarer twice. The collector calls the supported unwrap() accessor first.
• An owner that is not a submodule at all (Qwen3-TTS's graph wrapper, ming's MingAudioGenerator) is reached by one explicit hop from the module that holds it.

Adding a model¶

Return one entry per distinct allocation. Prefer spec_from_hf_config() and pass the same config object the cache is built from; it raises rather than guessing when an attribute is missing. capacity_source is diagnostic text -- never branch on it.

Known limits¶

Row counts taken from live state after warmup miss caches transformers has not yet materialized, which makes Qwen3-TTS's graph-pool figure a floor for the xvec path.

MiMo's graph-pool cache has no reachable Python object -- it is a local inside the captured frame -- so its width comes from the captured bucket list rather than from tensors. Its two entries are also mutually exclusive per call: a call either replays a bucket or runs eagerly. The sum is the right ceiling for the process and the wrong number for a single request.

MiMo captures every bucket gated only on torch.cuda.is_available() (mimo_audio_llm.py:670), so those 179.44 MiB stay resident even when the deploy config sets enforce_eager: true. Gating it is a one-line change that needs a model boot to verify, so it is noted rather than bundled here.