FitMyLLM
NVIDIA· TURING

NVIDIA CMP 30HX

Running LLMs on the CMP 30HX — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
6 GB
BUDGET
BANDWIDTH
336
GB/S
MODELS Q4
130/449
29%
7B Q4 SPEED
~38
FAST
▸ MODEL COVERAGE @ Q429% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~90
TOK/S
7B
~38
TOK/S
14B
7.9GB NEEDED
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
6 GB
BANDWIDTH
336 GB/s
FP16 COMPUTE
10.1 TFLOPS
TDP
125W
MEMORY
GDDR6
ARCHITECTURE
Turing
CUDA CORES
1,408
PCIE
Gen 1 x4
130
FAST MODELS · >30 TOK/S
Real-time chat speed
130
USABLE · >10 TOK/S
Comfortable for all tasks
130
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying CMP 30HX 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· 130
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2987
TOK/S · 9% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1991
TOK/S · 10% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1920
TOK/S · 10% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1920
TOK/S · 10% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1050
TOK/S · 11% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
996
TOK/S · 11% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
881
TOK/S · 11% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
802
TOK/S · 12% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
802
TOK/S · 12% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
802
TOK/S · 12% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
768
TOK/S · 12% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
747
TOK/S · 12% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
707
TOK/S · 12% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
538
TOK/S · 13% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
538
TOK/S · 13% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
517
TOK/S · 13% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
473
TOK/S · 14% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
473
TOK/S · 14% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
448
TOK/S · 14% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
448
TOK/S · 14% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
448
TOK/S · 14% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
434
TOK/S · 14% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
363
TOK/S · 16% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
358
TOK/S · 16% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
332
TOK/S · 16% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
309
TOK/S · 17% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
286
TOK/S · 18% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
249
TOK/S · 19% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
244
TOK/S · 19% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
224
TOK/S · 20% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
224
TOK/S · 20% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
224
TOK/S · 20% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
224
TOK/S · 20% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
207
TOK/S · 21% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
207
TOK/S · 21% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
207
TOK/S · 21% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
199
TOK/S · 22% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
189
TOK/S · 23% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
189
TOK/S · 23% VRAM
S
granite-4.0-h-tiny 6.9B6.9BMoE
GRANITE·128K CTX· CHAT
179
TOK/S · 78% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your CMP 30HX.

WHAT THIS CARD IS WORTH

CMP 30HX holds 130 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Falcon-H1 7B 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
Falcon-H1 7B7.59B · Q4_K_M5.4 GB38 tok/sEST
Falcon-H1R 7B7.59B · Q4_K_M5.4 GB38 tok/sEST
Falcon Mamba 7B7.27B · Q4_K_M5.0 GB40 tok/sEST
WizardLM 2 7B7B · Q4_K_M5.3 GB42 tok/sEST
StarCoder2 7B7B · Q4_K_M5.0 GB42 tok/sEST
Dolly v2 7B6.9B · Q4_K_M5.3 GB42 tok/sEST
granite-4.0-h-tiny 6.9B6.9B · Q4_K_M5.0 GB195 tok/sEST
ChatGLM2 6B6.24B · Q5_K_M5.1 GB40 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3080 10GB10 GB$42917.7 tok/s per $100
Arc B57010 GB$2198.2 tok/s per $100
Radeon RX 670010 GB$29912.0 tok/s per $100
Radeon RX 6750 GRE 10 GB10 GB$22915.7 tok/s per $100
CMP 30HX6 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 30HX — 6 GB VRAM.

CMP 30HX SPEC
BRAND
NVIDIA
VRAM
6 GB GDDR6
BANDWIDTH
336 GB/s
FP16 COMPUTE
10.1 TFLOPS
FP32 COMPUTE
5 TFLOPS
CUDA CORES
1,408
TDP
125 W
ARCHITECTURE
Turing
▸ AI CAPABILITY
130/ 449 models @ Q4

With 6 GB VRAM and 336 GB/s bandwidth, this GPU handles models up to 7B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~38 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR CMP 30HX
130 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Alpaca 7B7B4.8 GB3827.7
Baichuan2 7B7B4.8 GB3821.5
Vicuna 7B7B4.8 GB3822.0
MPT-7B7B4.8 GB387.8
Orca 2 7B7B4.8 GB3826.1
WizardLM 2 7B7B4.8 GB3826.1
StarCoder2 7B7B4.8 GB3817.0
WizardCoder Python 7B7B4.8 GB3853.7
WizardLM 7B7B4.8 GB3815.5
OLMo 3.1 RLZero 7B Code7B4.8 GB3821.8
OLMo 3.1 RLZero 7B Math7B4.8 GB3821.8
Dolly v2 7B6.9B4.7 GB397.0
granite-4.0-h-tiny 6.9B6.9B4.7 GB17949.2
Llama 2 7B6.74B4.6 GB4021.1
CodeLlama 7B6.74B4.6 GB4028.1
LLaMA 1 7B6.74B4.6 GB4030.8
DeepSeek Coder 6.7B6.7B4.6 GB4023.6
OPT 6.7B6.7B4.6 GB4018.5
ChatGLM2 6B6.24B4.3 GB4320.7
ChatGLM3 6B6.24B4.3 GB4342.7