NVIDIA CMP 170HX 64GB (unlocked)

reported speed:

117.0 tokens/s generation · 6066.0 tokens/s prompt processing

kv:

fp8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports DeepSeek-V4.1-Flash at 117 tok/s decode on one stream at 128k context and 6,066 tok/s prefill at 105k tokens on eight 64 GB CMP 170HX mining cards. Setup is vLLM with fp8 KV cache, PP=8, DSpark speculative decoding with 5 draft tokens, Engram tables in pinned host RAM, and 1M context. Decode drops to 96 tok/s at 512k. Eight concurrent streams reach 532 tok/s aggregate (66 per stream) at 105k. KV pool holds 6.17M tokens. One card was capped to 180 W after PCIe bus drops.

reported speed:

101.3 tokens/s generation · 3191.0 tokens/s prompt processing

quant:

W4A16 (W4A16)

kv:

BF16

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.8-Flash-Next at 101.3 t/s decode on Japanese prose and 170.0 t/s on code, single stream, on two NVIDIA CMP 170HX cards. Setup is vLLM with W4A16 weights, BF16 KV cache, MTP k=4 speculative decoding, 262,144-token context, and expert parallel across the two cards. The PLE n-gram table was converted to FP8 locally to fit in 92 GiB of host RAM. Aggregate throughput reaches 392 t/s at 4 concurrent requests. Prefill measures 3,191 t/s at 6,954 tokens and 3,261 t/s at 27,853 tokens. A 200,087-token prompt returned the planted code with 70.5 s time to first token and 69.0 t/s decode at depth. MTP is worth about 1.6x on prose and 2.6x on code. The user notes xhigh reasoning effort spent the whole budget without answering in 5 of 6 runs.

reported speed:

95.0 tokens/s generation

quant:

FP8

kv:

FP8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports DeepSeek V4 Flash Vision at up to ~95 tok/s on 4× CMP 170HX 64GB (256GB aggregate HBM2e). Setup is vLLM with pipeline parallel 4, max_model_len 262144, FP8 KV cache, DSpark speculative decoding with 6 tokens, and max_num_seqs 2. Normal coding workload runs ~50–70 tok/s, strong speculative-decoding periods ~80–90 tok/s, peak ~95 tok/s. Cards sit around 56–62GB VRAM each at 50–60°C. Next tests planned for Qwen3.8 Flash Next and GLM 5.3.

reported speed:

116.0 tokens/s generation · 6800-7950 tokens/s prompt processing

quant:

W4A16

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3-Next-80B at 116 t/s single-stream decode on a single NVIDIA CMP 170HX with 64 GB HBM2e, at a 150 W cap. Setup is vLLM 0.27.1 with W4A16 weights (40.9 GB), torch 2.13.0+cu130, CUDA 13.0, Ubuntu 26.04 LTS, on an AMD Ryzen Threadripper PRO 3945WX with 128 GB DDR4 ECC. Prefill over about 8.9k tokens measured 6800 to 7950 t/s; power draw 137 to 145 W. Aggregate throughput at 8 concurrent requests was 352 t/s, saturating at 4 slots. Also measured on the same card for context: Ornith-1.5-35B FP8 at 122.5 t/s and Qwen3.8-27B W4A16 with DFlash2 at 127 t/s single-stream.

reported speed:

98.1 tokens/s generation · 5300.0 tokens/s prompt processing

quant:

MXFP4+FP8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports DeepSeek-V4-Flash-0731 at 98.1 t/s single-stream decode on 4x CMP 170HX (64 GB HBM2e each), up from a 50.8 t/s baseline with DSpark speculative decoding. Setup is vLLM with pipeline parallelism, native MXFP4+FP8 weights (~155.4 GiB), context verified to 1,047,736 tokens. At 100k context single-stream decode is 38.8 t/s with 14.6 s time to first token. Aggregate throughput across 64 concurrent requests is 712.8 t/s with DSpark (472.0 t/s baseline), and 90.0 t/s at 100k context. Prefill across the 25k-77k context range is about 5,300 t/s.

reported speed:

147.0 tokens/s generation

quant:

W4A16 (W4A16)

kv:

FP8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.8-27B at 147.0 t/s single-stream decode on one NVIDIA CMP 170HX 64GB, with 134.7 t/s at 4K, 100.1 t/s at 65K, ~90 t/s at 126K, and 64.9 t/s at 250K context. Setup is vLLM 0.27.1 with a W4A16 target, a DFlash2 W4A16 drafter, FP8 target KV, BF16 draft KV, a custom SM80 split-KV verifier, full CUDA Graph, 35 verifier segments / 140 CTAs, k=3 draft tokens, MAX_SEQS=1, and 1350 MHz / 180W. These are decode-only numbers, not end-to-end throughput including prefill. FP8 beat INT8 at long context (48.3 vs 115.4 ms/iter at 250K). Allowing 2 concurrent long requests made makespan and slowest-request throughput worse at both 126K and 250K.

reported speed:

50.0 tokens/s generation

quant:

Q8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.8 27B at around 50 t/s on a CMP 170HX, running at Q8 or BF16. The user finds the model hallucinates classes and APIs in 90%+ of answers to domain-specific enterprise Java questions, while DeepSeek and Microsoft Copilot produce working code 99% of the time. The user asks whether Qwen's agentic optimization is responsible and requests tips for a harness, system prompt, or skills to reduce hallucination.

reported speed:

63.0 tokens/s generation · 1700.0 tokens/s prompt processing

quant:

Q6_K

kv:

F16

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports Qwen3.6-35B-A3B at 63 t/s on 4x CMP 170HX 8GB cards flashed to 64GB each, 256GB total. Setup uses MTP with little-MoE default. Generation reaches 110 t/s with MTP optimistic.

reported speed:

29.0 tokens/s generation · 450.0 tokens/s prompt processing

quant:

Q4_K_XL

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports testing multiple models on 4x CMP 170HX 64GB cards, including DeepSeek V4-Flash 0731 with a Q4_K_XL quant, 13B active MoE, and 1M context, plain no-spec. The cards are cut-down A100 mining cards with 64GB each, PCIe Gen2 x4, and no NVLink. Other models tested include gpt-oss-120B, Qwen3.6-35B-A3B, GLM-4.5-Air, and MiniMax-M2.7.

reported speed:

3468.0 tokens/s prompt processing

quant:

FP8

Reported by the source; GPU count, offloading and concurrent requests can change this figure. Check the full setup before comparing.

User reports an unlocked CMP 170HX, A100 silicon with a firmware unlock, reaching 193 TFLOPS tensor throughput, up from 6.3 TFLOPS. In llama.cpp, pp512 rises from 599.6 to 3468 tok/s. Serving Qwen3.8-27B-FP8 under vLLM, one 170HX beats a 2x3090 tensor-parallel pair on prefill at 197W versus 454W.