FitMyLLM
NVIDIA· FERMI 2.0

NVIDIA Tesla M2090

VRAM
6 GB
BUDGET
BANDWIDTH
177
GB/S
MODELS Q4
130/449
29%
7B Q4 SPEED
~20
GOOD
▸ 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
~47
TOK/S
7B
~20
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
177 GB/s
FP16 COMPUTE
1.3 TFLOPS
TDP
250W
MEMORY
GDDR5
ARCHITECTURE
Fermi 2.0
CUDA CORES
512
PCIE
Gen 2 x16
106
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 Tesla M2090 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
1573
TOK/S · 9% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1049
TOK/S · 10% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1011
TOK/S · 10% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1011
TOK/S · 10% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
553
TOK/S · 11% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
524
TOK/S · 11% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
464
TOK/S · 11% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
423
TOK/S · 12% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
423
TOK/S · 12% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
423
TOK/S · 12% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
405
TOK/S · 12% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
393
TOK/S · 12% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
373
TOK/S · 12% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
283
TOK/S · 13% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
283
TOK/S · 13% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
272
TOK/S · 13% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
249
TOK/S · 14% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
249
TOK/S · 14% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
236
TOK/S · 14% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
236
TOK/S · 14% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
236
TOK/S · 14% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
228
TOK/S · 14% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
191
TOK/S · 16% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
189
TOK/S · 16% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
175
TOK/S · 16% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
163
TOK/S · 17% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
151
TOK/S · 18% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
131
TOK/S · 19% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
129
TOK/S · 19% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
118
TOK/S · 20% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
118
TOK/S · 20% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
118
TOK/S · 20% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
118
TOK/S · 20% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
109
TOK/S · 21% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
109
TOK/S · 21% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
109
TOK/S · 21% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
105
TOK/S · 22% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
100
TOK/S · 23% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
100
TOK/S · 23% VRAM
S
granite-4.0-h-tiny 6.9B6.9BMoE
GRANITE·128K CTX· CHAT
94
TOK/S · 78% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla M2090.

WHAT THIS CARD IS WORTH

Tesla M2090 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 GB24 tok/sEST
Falcon-H1R 7B7.59B · Q4_K_M5.4 GB24 tok/sEST
Falcon Mamba 7B7.27B · Q4_K_M5.0 GB25 tok/sEST
WizardLM 2 7B7B · Q4_K_M5.3 GB26 tok/sEST
StarCoder2 7B7B · Q4_K_M5.0 GB26 tok/sEST
Dolly v2 7B6.9B · Q4_K_M5.3 GB26 tok/sEST
granite-4.0-h-tiny 6.9B6.9B · Q4_K_M5.0 GB120 tok/sEST
ChatGLM2 6B6.24B · Q5_K_M5.1 GB25 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
Tesla M20906 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 Tesla M2090 — 6 GB VRAM.

TESLA M2090 SPEC
BRAND
NVIDIA
VRAM
6 GB GDDR5
BANDWIDTH
177 GB/s
FP16 COMPUTE
1.3 TFLOPS
FP32 COMPUTE
1.3 TFLOPS
CUDA CORES
512
TDP
250 W
ARCHITECTURE
Fermi 2.0
▸ AI CAPABILITY
130/ 449 models @ Q4

With 6 GB VRAM and 177 GB/s bandwidth, this GPU handles models up to 7B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~20 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA M2090
130 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Alpaca 7B7B4.8 GB2027.7
Baichuan2 7B7B4.8 GB2021.5
Vicuna 7B7B4.8 GB2022.0
MPT-7B7B4.8 GB207.8
Orca 2 7B7B4.8 GB2026.1
WizardLM 2 7B7B4.8 GB2026.1
StarCoder2 7B7B4.8 GB2017.0
WizardCoder Python 7B7B4.8 GB2053.7
WizardLM 7B7B4.8 GB2015.5
OLMo 3.1 RLZero 7B Code7B4.8 GB2021.8
OLMo 3.1 RLZero 7B Math7B4.8 GB2021.8
Dolly v2 7B6.9B4.7 GB217.0
granite-4.0-h-tiny 6.9B6.9B4.7 GB9449.2
Llama 2 7B6.74B4.6 GB2121.1
CodeLlama 7B6.74B4.6 GB2128.1
LLaMA 1 7B6.74B4.6 GB2130.8
DeepSeek Coder 6.7B6.7B4.6 GB2123.6
OPT 6.7B6.7B4.6 GB2118.5
ChatGLM2 6B6.24B4.3 GB2320.7
ChatGLM3 6B6.24B4.3 GB2342.7