FitMyLLM
▸ NVIDIA· FERMI

NVIDIA Tesla M2070-Q

Running LLMs on the Tesla M2070-Q — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
6 GB
BUDGET
BANDWIDTH
150
GB/S
MODELS Q4
130/449
29%
7B Q4 SPEED
~17
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
~40
TOK/S
7B
~17
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
150 GB/s
FP16 COMPUTE
1 TFLOPS
TDP
225W
MEMORY
GDDR5
ARCHITECTURE
Fermi
CUDA CORES
448
PCIE
Gen 2 x16
100
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 M2070-Q 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
1333
TOK/S · 9% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
889
TOK/S · 10% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
857
TOK/S · 10% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
857
TOK/S · 10% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
469
TOK/S · 11% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
444
TOK/S · 11% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
393
TOK/S · 11% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
358
TOK/S · 12% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
358
TOK/S · 12% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
358
TOK/S · 12% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
343
TOK/S · 12% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
333
TOK/S · 12% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
316
TOK/S · 12% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
240
TOK/S · 13% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
240
TOK/S · 13% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
231
TOK/S · 13% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
211
TOK/S · 14% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
211
TOK/S · 14% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
200
TOK/S · 14% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
200
TOK/S · 14% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
200
TOK/S · 14% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
194
TOK/S · 14% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
162
TOK/S · 16% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
160
TOK/S · 16% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
148
TOK/S · 16% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
138
TOK/S · 17% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
128
TOK/S · 18% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
111
TOK/S · 19% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
109
TOK/S · 19% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
100
TOK/S · 20% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
100
TOK/S · 20% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
100
TOK/S · 20% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
100
TOK/S · 20% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
92
TOK/S · 21% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
92
TOK/S · 21% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
92
TOK/S · 21% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
89
TOK/S · 22% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
85
TOK/S · 23% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
85
TOK/S · 23% VRAM
›
S
granite-4.0-h-tiny 6.9B6.9BMoE
GRANITE·128K CTX· CHAT
80
TOK/S · 78% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla M2070-Q.

WHAT THIS CARD IS WORTH

Tesla M2070-Q 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 GB21 tok/sEST
Falcon-H1R 7B7.59B · Q4_K_M5.4 GB21 tok/sEST
Falcon Mamba 7B7.27B · Q4_K_M5.0 GB22 tok/sEST
WizardLM 2 7B7B · Q4_K_M5.3 GB23 tok/sEST
StarCoder2 7B7B · Q4_K_M5.0 GB23 tok/sEST
Dolly v2 7B6.9B · Q4_K_M5.3 GB23 tok/sEST
granite-4.0-h-tiny 6.9B6.9B · Q4_K_M5.0 GB106 tok/sEST
ChatGLM2 6B6.24B · Q5_K_M5.1 GB22 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 M2070-Q6 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 M2070-Q — 6 GB VRAM.

▸ TESLA M2070-Q SPEC
BRAND
NVIDIA
VRAM
6 GB GDDR5
BANDWIDTH
150 GB/s
FP16 COMPUTE
1 TFLOPS
FP32 COMPUTE
1 TFLOPS
CUDA CORES
448
TDP
225 W
ARCHITECTURE
Fermi
▸ AI CAPABILITY
130/ 449 models @ Q4

With 6 GB VRAM and 150 GB/s bandwidth, this GPU handles models up to 7B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~17 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA M2070-Q
130 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Alpaca 7B7B4.8 GB1727.7
Baichuan2 7B7B4.8 GB1721.5
Vicuna 7B7B4.8 GB1722.0
MPT-7B7B4.8 GB177.8
Orca 2 7B7B4.8 GB1726.1
WizardLM 2 7B7B4.8 GB1726.1
StarCoder2 7B7B4.8 GB1717.0
WizardCoder Python 7B7B4.8 GB1753.7
WizardLM 7B7B4.8 GB1715.5
OLMo 3.1 RLZero 7B Code7B4.8 GB1721.8
OLMo 3.1 RLZero 7B Math7B4.8 GB1721.8
Dolly v2 7B6.9B4.7 GB177.0
granite-4.0-h-tiny 6.9B6.9B4.7 GB8049.2
Llama 2 7B6.74B4.6 GB1821.1
CodeLlama 7B6.74B4.6 GB1828.1
LLaMA 1 7B6.74B4.6 GB1830.8
DeepSeek Coder 6.7B6.7B4.6 GB1823.6
OPT 6.7B6.7B4.6 GB1818.5
ChatGLM2 6B6.24B4.3 GB1920.7
ChatGLM3 6B6.24B4.3 GB1942.7