Format is compressed-tensors / float-quantized: per-output-channel static FP8 weight scales plus dynamic per-token activation scales — the same layout llm-compressor's FP8_DYNAMIC recipe produces. Served directly by vLLM via CompressedTensorsW8A8Fp8.

Read this before using it

FP8 saves only ~11% of file size here, and buys no measured speed. This is a 3.02 B-parameter draft in which embed_tokens and lm_head (163840x7168 each) are 2.35 B params — 78% of the model — and neither is quantizable. Only the 22% in linear layers converts:

	size
bf16 original	5.617 GiB
this FP8 model	5.004 GiB (10.9% smaller)

Measured per-user decode throughput is indistinguishable from the bf16 original (see below). Use this model for kernel-path compatibility with an FP8 serving stack, not to save memory or gain speed.

What is quantized

Quantized to float8_e4m3fn with per-channel scales (8 tensors): fc, mlp.{gate,up,down}_proj, self_attn.{q_a_proj,q_b_proj,kv_a_proj_with_mqa,o_proj}.

Left in bf16, bit-identical to the source (9 tensors):

embed_tokens, lm_head — vLLM builds the Eagle3 ParallelLMHead with no quant_config, so a quantized head would never be read.
kv_b_proj — MLA absorbs it into W_UK/W_UV at load time via get_and_maybe_dequant_weights(); for compressed-tensors that takes a generic O(N^3) identity-matmul dequant path. It is 8.4 M params (0.3%), so bf16 costs nothing and avoids that path.
all RMSNorm weights.

Expressed in the config as ignore: ["lm_head", "re:.*kv_b_proj.*"].

strategy: channel is deliberate, not incidental: vLLM fuses q_a_proj+kv_a_proj_with_mqa into fused_qkv_a_proj and gate_proj+up_proj into gate_up_proj. Channel scales load through ChannelQuantScaleParameter (a _ColumnvLLMParameter) and concatenate per shard along dim 0. A per-tensor scheme would require the fused shards to share one scale.

Weight-level fidelity

512 tokens of N(0,1) activations, dequantized-FP8 matmul vs bf16, fp32 reference, worst case across all 8 quantized layers:

metric	worst
cosine similarity	0.999648
output relative L2 error	2.654%

Error is uniform (2.644–2.654%) across every layer — the E4M3 floor for Gaussian weights under per-channel scaling, not a per-layer defect.

Evaluation

vLLM 0.20.0, verifier Kimi-K2.6, 8xH200, TP=1 / EP=8 / DP=8, kv-cache-dtype fp8, max-model-len 32768, num_speculative_tokens=3, greedy. Four held-out test sets derived from real Kimi-K2.6 production traffic (1000 conversations each; accept_len over a 128-prompt subsample).

accept_length (higher is better):

model	random	general-chat	multimodal	agentic
lightseekorg/kimi-k2.6-eagle3-mla (init)	2.349	2.385	2.469	2.363
k-l-lambda/kimi-k2.6-eagle3-mla (bf16)	2.380	2.383	2.468	2.409
this model (FP8)	2.400	2.393	2.496	2.403

Deltas vs bf16: +0.020 / +0.010 / +0.028 / −0.006. Mixed signs, all within ±0.03 — read this as quantization costs no measurable accept_length, not as FP8 being better. Run-to-run variance at this subsample size has not been measured, so ±0.03 is not an established error bar.

per-position acceptance (pos0/pos1/pos2 %):

model	random	general-chat	multimodal	agentic
bf16	66/43/29	66/43/29	68/46/32	67/44/30
FP8	67/44/29	66/44/29	69/48/33	67/44/29

per-user decode TPS (speedup vs no-spec baseline):

c	model	random	general-chat	multimodal	agentic
128	no-spec baseline	7.84	7.93	10.02	10.69
128	bf16	9.74 (1.24x)	8.79 (1.11x)	10.88 (1.09x)	10.02 (0.94x)
128	FP8	9.62 (1.23x)	8.83 (1.11x)	10.89 (1.09x)	9.90 (0.93x)
192	no-spec baseline	6.54	6.46	8.59	8.43
192	bf16	7.69 (1.18x)	7.17 (1.11x)	8.93 (1.04x)	8.57 (1.02x)
192	FP8	7.83 (1.20x)	7.27 (1.13x)	8.86 (1.03x)	8.16 (0.97x)
256	no-spec baseline	5.57	5.62	7.23	7.31
256	bf16	6.76 (1.21x)	6.32 (1.12x)	7.93 (1.10x)	7.37 (1.01x)
256	FP8	6.97 (1.25x)	6.36 (1.13x)	8.08 (1.12x)	7.39 (1.01x)

FP8-vs-bf16 TPS differences scatter in both directions (max |Δ| 0.41 tok/s) with no consistent sign by dataset or concurrency. Expected: the draft forward pass is a small fraction of each speculation step, and only 22% of its params are quantized, so the kernel gain does not surface.

Note on the long-conversation set: speculative decoding is a net loss on agentic at c=128 (0.93–0.94x) for all three drafts, breaking even only at higher concurrency. That workload averages 28.5 turns with 100% tool use and has the highest no-spec baseline, leaving least headroom for drafting to win. This is a property of the workload, not of quantization.

Requirements

Compute capability >= 89 (Ada / Hopper). CompressedTensorsW8A8Fp8 reports min_capability 89; H200 is 90.
vLLM with draft-model quantization support. Verified on 0.20.0 (compressed_tensors 0.15.0.1), where deepseek_eagle3.py calls get_draft_quant_config() so the draft's own quant config is honored rather than the verifier's.

Usage

bash

vllm serve /path/to/Kimi-K2.6 \
  --speculative-config '{"method":"eagle3","model":"k-l-lambda/kimi-k2.6-eagle3-mla-fp8","num_speculative_tokens":3}'

Inherited caveat

The bf16 parent is a K2.6 fine-tune selected by validation loss on the K2.6 teacher distribution, and its card notes it can over-specialize: on K2.7-Code production traffic the official lightseekorg init showed higher accept-length. The evaluation above is K2.6 traffic, where the fine-tune is ahead or level — it does not test, and so does not refute, the K2.7 claim. Benchmark against your own traffic before choosing a draft.