FitMyLLM
NVIDIA· TURING

NVIDIA CMP 50HX

Running LLMs on the CMP 50HX — 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
560
GB/S
MODELS Q4
223/449
50%
7B Q4 SPEED
~64
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
~149
TOK/S
7B
~64
TOK/S
14B
~32
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
560 GB/s
FP16 COMPUTE
22.1 TFLOPS
TDP
250W
MEMORY
GDDR6
ARCHITECTURE
Turing
CUDA CORES
3,584
TENSOR CORES
448
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 50HX 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.

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▸ COMPATIBLE MODELS· 223
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
4978
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
3319
TOK/S · 6% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
3200
TOK/S · 6% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
3200
TOK/S · 6% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1750
TOK/S · 6% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1659
TOK/S · 7% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1469
TOK/S · 7% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
1337
TOK/S · 7% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
1337
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
1337
TOK/S · 7% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1280
TOK/S · 7% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1244
TOK/S · 7% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1179
TOK/S · 7% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
896
TOK/S · 8% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
896
TOK/S · 8% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
862
TOK/S · 8% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
789
TOK/S · 8% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
789
TOK/S · 8% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
747
TOK/S · 9% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
747
TOK/S · 9% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
747
TOK/S · 9% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
723
TOK/S · 9% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
605
TOK/S · 9% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
597
TOK/S · 9% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
553
TOK/S · 10% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
515
TOK/S · 10% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
477
TOK/S · 11% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
415
TOK/S · 11% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
407
TOK/S · 12% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
373
TOK/S · 12% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
373
TOK/S · 12% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
373
TOK/S · 12% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
373
TOK/S · 12% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
345
TOK/S · 13% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
345
TOK/S · 13% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
345
TOK/S · 13% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
332
TOK/S · 13% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
315
TOK/S · 14% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
315
TOK/S · 14% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
299
TOK/S · 56% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your CMP 50HX.

WHAT THIS CARD IS WORTH

CMP 50HX 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 GB36 tok/sEST
Dolly v2 12B12B · Q4_K_S8.7 GB38 tok/sEST
StableLM 2 12B12B · Q4_K_M9.0 GB36 tok/sEST
Falcon2 11B11B · Q4_K_M8.5 GB40 tok/sEST
SOLAR-10.7B10.7B · Q4_K_M8.1 GB41 tok/sEST
Falcon3-10B10.3B · Q5_K_M8.8 GB36 tok/sEST
GLM-4.1V 9B Thinking10.29B · Q4_K_M8.9 GB42 tok/sEST
Bamba 9B v29.78B · Q5_K_M8.3 GB38 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 50HX10 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 50HX — 10 GB VRAM.

CMP 50HX SPEC
BRAND
NVIDIA
VRAM
10 GB GDDR6
BANDWIDTH
560 GB/s
FP16 COMPUTE
22.1 TFLOPS
FP32 COMPUTE
11.1 TFLOPS
CUDA CORES
3,584
TENSOR CORES
448
TDP
250 W
ARCHITECTURE
Turing
▸ AI CAPABILITY
223/ 449 models @ Q4

With 10 GB VRAM and 560 GB/s bandwidth, this GPU handles models up to 12.2B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~64 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR CMP 50HX
223 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Mistral-Nemo 12.2B12.2B7.9 GB3722.4
Dolly v2 12B12B7.8 GB376.4
StableLM 2 12B12B7.8 GB3721.3
Falcon2 11B11B7.2 GB4133.2
SOLAR-10.7B10.7B7.0 GB4228.2
Falcon3-10B10.3B6.8 GB4338.2
Bamba 9B v29.78B6.5 GB4626.1
Qwen 3.5 9B9.65B6.4 GB4650.6
RecurrentGemma 9B9.63B6.4 GB4735.0
glm-4-9b9.4B6.2 GB4820.5
MiniCPM-o 4.59.37B6.2 GB48
gemma-2-9b9.2B6.1 GB4930.2
Yi 1.5 9B9B6.0 GB5030.3
Yi Coder 9B9B6.0 GB5035.8
Ministral 3 8B8.92B5.9 GB5025.7
Ministral 3 8B Reasoning8.92B5.9 GB50
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB5044.2
InternLM3 8B Instruct8.8B5.9 GB5138.7
Qwen3-VL 8B Instruct8.77B5.8 GB5126.4
MiniCPM-V 4.58.7B5.8 GB5126.1