Qwen3.5-27B-FP8¶

Qwen's 27B dense hybrid model (48 Gated DeltaNet layers and 16 attention layers) with vision and reasoning, from the official FP8 checkpoint, at its native 256K context on a single Gaudi 2 card, 32 sequences at a time. Prefix caching is on through the gdn-prefix-cache fix.


Model	Qwen/Qwen3.5-27B-FP8
Hardware	1x Gaudi 2 (TP 1)
Image	vllm-gaudi `v0.26.0` (`b059f9c`), vLLM `568afb3a`, Gaudi SW 1.24.1
Precision	FP8 checkpoint (block 128 x 128, dynamic activations)
Context	262,144 (native)
Concurrency	`--max-num-seqs 32`
Parsers	tools `qwen3_coder`, reasoning `qwen3`
Recipe	`recipes/Qwen/Qwen3.5-27B-FP8/recipe.yaml`

Qwen3.6-27B-FP8 has the same geometry; its recipe adds strict think-end parsing and whitespace-free forced tool calls on top of this one.

Prefix caching¶

On the stock release, prefix-cache hits on the Gated DeltaNet layers resume from the wrong state, so prefix caching is only correct with gdn-prefix-cache. The fix keeps the GDN layers in their compact per-request slots (VLLM_COMPACT_GDN=1) and stores a state checkpoint at the end of every full KV block in that block's own attention page. A hit restores the checkpoint before the next chunk, and the full KV pool stays available. With caching on, the hybrid KV block is 896 tokens and prefill chunks end on block boundaries; prompts shorter than one block never hit. The design and the upstream status are in Prefix caching on Gated DeltaNet hybrids.

Launch¶

$COOKBOOK is a checkout of this repository inside the container.

export PT_HPU_LAZY_MODE=0
export PT_HPU_ENABLE_LAZY_COLLECTIVES=true
export VLLM_USE_NAIVE_MAMBA_CACHE_SHARING=1
export VLLM_COMPACT_GDN=1
export PT_HPUGRAPH_DISABLE_TENSOR_CACHE=false
export VLLM_SKIP_WARMUP=true
export VLLM_GRAPH_RESERVED_MEM=0.3
export VLLM_EXPONENTIAL_BUCKETING=True
export VLLM_PROMPT_BS_BUCKET_MIN=1
export VLLM_PROMPT_BS_BUCKET_STEP=1
export VLLM_PROMPT_BS_BUCKET_MAX=8
export VLLM_DECODE_BS_BUCKET_MIN=1
export VLLM_DECODE_BS_BUCKET_STEP=1
export VLLM_DECODE_BS_BUCKET_MAX=32
export VLLM_PROMPT_SEQ_BUCKET_MIN=128
export VLLM_PROMPT_SEQ_BUCKET_STEP=4096
export VLLM_PROMPT_SEQ_BUCKET_MAX=262144
export VLLM_DECODE_BLOCK_BUCKET_MIN=128
export VLLM_DECODE_BLOCK_BUCKET_STEP=1024
export VLLM_DECODE_BLOCK_BUCKET_MAX=16384
export VLLM_ENGINE_ITERATION_TIMEOUT_S=3600
export VLLM_RPC_TIMEOUT=3600000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=1800

# Fixes: the v0.26.0 profile plus this recipe's opt-in fixes, in the pack's order.
# Stops with a non-zero exit code if the image does not match.
python3 "$COOKBOOK/fixes/apply_all.py" --profile v0.26.0 gdn-prefix-cache || exit 1

vllm serve Qwen/Qwen3.5-27B-FP8 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --max-num-seqs 32 \
  --gpu-memory-utilization 0.85 \
  --block-size 128 \
  --max-num-batched-tokens 1792 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --enable-prompt-tokens-details \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --limit-mm-per-prompt '{"image":16,"video":0}'

Compile mode (PT_HPU_LAZY_MODE=0). The GDN layers lose their state cache at the first decode in lazy mode.
VLLM_COMPACT_GDN=1: the GDN layers keep their state in compact per-request slots, and gdn-prefix-cache stores the checkpoints in the attention pages, so the whole pool stays available. gdn-prefix-cache refuses to start without it.
16 images per prompt is the worst-case context clamp (16,384 tokens per maximum-size image).
With prefix caching on, prefill chunks end on 896-token blocks: 1,792 tokens is exactly two blocks per chunk.

Buckets¶

No bucket file. Prompt batch buckets 1 to 8, decode batch buckets 1 to 32, prompt length 128 to 262,144 in steps of 4,096 with exponential bucketing, decode blocks 128 to 16,384 in steps of 1,024.

Required fixes¶

All from Fixes. The ones marked opt-in are named in the launch command; the rest come from --profile v0.26.0, which also applies the release's other generic fixes (see Using a recipe). The profile also runs seeded-sampling; this recipe does not depend on it (the seed segfault is a lazy-mode bug).

Fix	Why this model needs it
`prompt-order`	with prefix caching, a request that hits a block another request writes in the same step read it before it was written
`sampling-rows-refresh`	stale device sampling rows after a batch reorder
`structured-output-rows`	structured-output steps sample each row with another request's parameters
`bad-words-index`	`bad_words` raises `IndexError` in the engine core
`logitsprocs-rows`	`logit_bias`, `min_p` and `min_tokens` are silently ignored
`async-discard-counter`	`AttributeError` on the first preemption under async scheduling
`resumed-prefill-logits`	a request resumed after preemption with prefix-cache hits samples again at every recomputed position, so the generated tail repeats
`hybrid-decode-pad`	when the hybrid KV block is split into 128-token kernel blocks, decode padding writes into a live block of another request
`gdn-prefix-cache` (opt-in)	prefix-cache hits on the GDN layers resume from a wrong state on the stock release; the fix stores and restores GDN checkpoints in the KV pages and zeroes new blocks and slots. It needs `hybrid-decode-pad` first
`gather-logprobs-int32`	the id corruption is lazy-mode only, but the fix is harmless here and keeps one fix set across recipes
`prompt-logprobs`	multimodal model with one prompt in a prefill batch returns flat hidden states; the first `prompt_logprobs` request kills the engine
`logprobs-decode-guard`	a bad logprob id from any source is replaced instead of stopping the API server
`rope-cpu-cache`	Gaudi fp32 cos/sin are wrong past about 1.5e5, so RoPE tables built on the HPU are wrong from position about 196K

Verified¶

On vllm-gaudi v0.26.0, one Gaudi 2 card, with exactly the launch command above in a container started from the stock image (the fixes applied at start-up). Requests went to the OpenAI-compatible API.

Check	Result
Known-answer prompts (arithmetic, facts, translation), temperature 0	8/8
The same prompts, 32 in flight	32/32
Tool call, `tool_choice: auto`, sampled at 0.6	5/5 valid `get_weather` calls with the right argument
Streamed tool call	name and JSON arguments assembled correctly
Tool result fed back	the answer uses the returned values
`top_logprobs: 20`	20 entries on every token
`prompt_logprobs: 5`	returned; server healthy afterwards
`response_format` JSON schema	valid object
`logit_bias` +100 on one token	takes effect
4 images, one solid colour each	colours named in order
16 images of 448 x 448 in one prompt	16/16 colours named in order
Prefix-cache hit vs cold (`cache_salt`), greedy, 8 prompts of 6K to 126K tokens with a needle	identical text and bit-identical logprobs 8/8; cached tokens a multiple of 896 on every hit
Needle in a haystack, 3 depths, cold	3/3 at 65K, 131K and 244K tokens
Server health after all checks	healthy, no restarts

KV pool at start-up: 623,359 tokens.

Performance¶

Single runs on the server above (measured as: per-call, one server; times are end to end and include time to first token).

Test	Result
One stream, short prompt, 256 tokens	47.1 tokens/s
32 streams, short prompts, 256 tokens each	575.3 tokens/s aggregate, 18.0 per stream
Cold prompt of 65K tokens (needle)	29 to 36 s to the answer
Cold prompt of 131K tokens (needle)	68 to 77 s to the answer
Cold prompt of 244K tokens (needle)	210 to 214 s to the answer

Known issues¶

Temperature 0 still varies with batch composition on HPU.
Give test requests a real token budget: this model reasons at length, and a short max_tokens returns empty content with finish_reason: length.

What not to do¶

Do not use lazy mode for this model.
Do not turn on prefix caching without gdn-prefix-cache, and do not set VLLM_COMPACT_GDN=0 with it.
Do not raise --max-num-batched-tokens to 3,584 or more at 256K: a cold prefill past about 131K context then died with PT_DEVMEM OOM: No enough memory for defragment.