FitMyLLM
▸ NVIDIA· AMPERE

NVIDIA CMP 170HX 10 GB

Running LLMs on the CMP 170HX 10 GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
10 GB
ENTRY-LEVEL
BANDWIDTH
1560
GB/S
MODELS Q4
223/449
50%
7B Q4 SPEED
~178
BLAZING
▸ MODEL COVERAGE @ Q450% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~416
TOK/S
7B
~178
TOK/S
14B
~89
TOK/S
32B
—
18.0GB NEEDED
70B
—
39.4GB NEEDED
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS →
▸ SPECIFICATIONS
VRAM
10 GB
BANDWIDTH
1560 GB/s
FP16 COMPUTE
50.5 TFLOPS
TDP
250W
MEMORY
HBM2e
ARCHITECTURE
Ampere
CUDA CORES
4,480
TENSOR CORES
280
PCIE
Gen 1 x4
223
FAST MODELS · >30 TOK/S
Real-time chat speed
223
USABLE · >10 TOK/S
Comfortable for all tasks
223
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying CMP 170HX 10 GB costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

Spin up in ~60s. Pay by the second. Cancel anytime.

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 223
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
13867
TOK/S · 5% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
9244
TOK/S · 6% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
8914
TOK/S · 6% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
8914
TOK/S · 6% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
4875
TOK/S · 6% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
4622
TOK/S · 7% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
4092
TOK/S · 7% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
3725
TOK/S · 7% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
3725
TOK/S · 7% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
3725
TOK/S · 7% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
3566
TOK/S · 7% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
3467
TOK/S · 7% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
3284
TOK/S · 7% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2496
TOK/S · 8% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2496
TOK/S · 8% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
2400
TOK/S · 8% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2197
TOK/S · 8% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2197
TOK/S · 8% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2080
TOK/S · 9% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2080
TOK/S · 9% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2080
TOK/S · 9% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2013
TOK/S · 9% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1686
TOK/S · 9% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1664
TOK/S · 9% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1541
TOK/S · 10% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1434
TOK/S · 10% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1328
TOK/S · 11% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1156
TOK/S · 11% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1135
TOK/S · 12% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1040
TOK/S · 12% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1040
TOK/S · 12% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1040
TOK/S · 12% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1040
TOK/S · 12% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
960
TOK/S · 13% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
960
TOK/S · 13% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
960
TOK/S · 13% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
924
TOK/S · 13% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
879
TOK/S · 14% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
879
TOK/S · 14% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
832
TOK/S · 56% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your CMP 170HX 10 GB.

WHAT THIS CARD IS WORTH

CMP 170HX 10 GB holds 223 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Mistral-Nemo 12.2B at Q4_K_M.

TOKENS/SEC PER $100
—
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
—
what memory costs on this card
THE BIGGEST IT TAKES
Mistral-Nemo 12.2B12.2B · Q4_K_M9.0 GB84 tok/sEST
Dolly v2 12B12B · Q4_K_S8.7 GB90 tok/sEST
StableLM 2 12B12B · Q4_K_M9.0 GB85 tok/sEST
Falcon2 11B11B · Q4_K_M8.5 GB93 tok/sEST
SOLAR-10.7B10.7B · Q4_K_M8.1 GB96 tok/sEST
Falcon3-10B10.3B · Q5_K_M8.8 GB85 tok/sEST
GLM-4.1V 9B Thinking10.29B · Q4_K_M8.9 GB100 tok/sEST
Bamba 9B v29.78B · Q5_K_M8.3 GB90 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
CMP 170HX 10 GB10 GB——

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA CMP 170HX 10 GB — 10 GB VRAM.

▸ CMP 170HX 10 GB SPEC
BRAND
NVIDIA
VRAM
10 GB HBM2e
BANDWIDTH
1560 GB/s
FP16 COMPUTE
50.5 TFLOPS
FP32 COMPUTE
12.6 TFLOPS
CUDA CORES
4,480
TENSOR CORES
280
TDP
250 W
ARCHITECTURE
Ampere
▸ AI CAPABILITY
223/ 449 models @ Q4

With 10 GB VRAM and 1560 GB/s bandwidth, this GPU handles models up to 12.2B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~178 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR CMP 170HX 10 GB
223 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Mistral-Nemo 12.2B12.2B7.9 GB10222.4
Dolly v2 12B12B7.8 GB1046.4
StableLM 2 12B12B7.8 GB10421.3
Falcon2 11B11B7.2 GB11333.2
SOLAR-10.7B10.7B7.0 GB11728.2
Falcon3-10B10.3B6.8 GB12138.2
Bamba 9B v29.78B6.5 GB12826.1
Qwen 3.5 9B9.65B6.4 GB12950.6
RecurrentGemma 9B9.63B6.4 GB13035.0
glm-4-9b9.4B6.2 GB13320.5
MiniCPM-o 4.59.37B6.2 GB133—
gemma-2-9b9.2B6.1 GB13630.2
Yi 1.5 9B9B6.0 GB13930.3
Yi Coder 9B9B6.0 GB13935.8
Ministral 3 8B8.92B5.9 GB14025.7
Ministral 3 8B Reasoning8.92B5.9 GB140—
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB14044.2
InternLM3 8B Instruct8.8B5.9 GB14238.7
Qwen3-VL 8B Instruct8.77B5.8 GB14226.4
MiniCPM-V 4.58.7B5.8 GB14326.1