FitMyLLM
▸ NVIDIA· FERMI

NVIDIA Tesla C2050

Running LLMs on the Tesla C2050 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
3 GB
BUDGET
BANDWIDTH
144
GB/S
MODELS Q4
81/449
18%
7B Q4 SPEED
~16
GOOD
▸ MODEL COVERAGE @ Q418% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~38
TOK/S
7B
—
3.9GB NEEDED
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
3 GB
BANDWIDTH
144 GB/s
FP16 COMPUTE
1 TFLOPS
TDP
238W
MEMORY
GDDR5
ARCHITECTURE
Fermi
CUDA CORES
448
PCIE
Gen 2 x16
81
FAST MODELS · >30 TOK/S
Real-time chat speed
81
USABLE · >10 TOK/S
Comfortable for all tasks
81
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Tesla C2050 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· 81
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1280
TOK/S · 18% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
853
TOK/S · 19% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
823
TOK/S · 19% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
823
TOK/S · 19% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
450
TOK/S · 21% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
427
TOK/S · 22% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
378
TOK/S · 22% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
344
TOK/S · 23% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
344
TOK/S · 23% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
344
TOK/S · 23% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
329
TOK/S · 23% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
320
TOK/S · 24% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
303
TOK/S · 24% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
230
TOK/S · 26% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
230
TOK/S · 26% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
222
TOK/S · 27% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
203
TOK/S · 28% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
203
TOK/S · 28% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
192
TOK/S · 29% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
192
TOK/S · 29% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
192
TOK/S · 29% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
186
TOK/S · 29% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
156
TOK/S · 31% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
154
TOK/S · 32% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
142
TOK/S · 33% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
132
TOK/S · 34% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
123
TOK/S · 35% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
107
TOK/S · 38% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
105
TOK/S · 39% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
96
TOK/S · 41% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
96
TOK/S · 41% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
96
TOK/S · 41% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
96
TOK/S · 41% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
89
TOK/S · 43% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
89
TOK/S · 43% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
89
TOK/S · 43% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
85
TOK/S · 44% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
81
TOK/S · 45% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
81
TOK/S · 45% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
77
TOK/S · 47% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla C2050.

WHAT THIS CARD IS WORTH

Tesla C2050 holds 81 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Falcon3-3B 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
Falcon3-3B3.23B · Q4_K_M2.6 GB48 tok/sEST
granite-4.0-h-micro 3.2B3.2B · Q4_K_M2.6 GB48 tok/sEST
Llama-3.2-3B3.2B · Q4_K_M2.7 GB48 tok/sEST
Falcon-H1 3B3.15B · Q5_K_M2.7 GB42 tok/sEST
Qwen 2.5 3B3.1B · Q5_K_M2.6 GB43 tok/sEST
SmolLM3-3B3.1B · Q4_K_M2.5 GB50 tok/sEST
Ministral 3B3B · Q4_K_M2.6 GB52 tok/sEST
StarCoder2 3B3B · Q5_K_M2.5 GB44 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
Tesla C20503 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 C2050 — 3 GB VRAM.

▸ TESLA C2050 SPEC
BRAND
NVIDIA
VRAM
3 GB GDDR5
BANDWIDTH
144 GB/s
FP16 COMPUTE
1 TFLOPS
FP32 COMPUTE
1 TFLOPS
CUDA CORES
448
TDP
238 W
ARCHITECTURE
Fermi
▸ AI CAPABILITY
81/ 449 models @ Q4

With 3 GB VRAM and 144 GB/s bandwidth, this GPU handles models up to 3.1B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~16 tok/s.

§ 01TOP MODELS FOR TESLA C2050
81 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Qwen 2.5 3B3.1B2.4 GB3737.2
SmolLM3-3B3.1B2.4 GB3730.5
Ministral 3B3B2.3 GB3829.6
StarCoder2 3B3B2.3 GB389.5
Granite 4.1 3B3B2.3 GB3816.6
xLAM-2 3B Function-Calling3B2.3 GB38—
Jamba 2 3B3B2.3 GB38—
Dolly v2 3B2.8B2.2 GB415.6
StableLM Zephyr 3B2.79B2.2 GB4114.9
Zephyr 3B2.79B2.2 GB4114.4
OPT 2.7B2.7B2.1 GB4328.0
Phi-2 2.7B2.7B2.1 GB4324.1
Zamba2 2.7B2.7B2.1 GB4348.0
Granite 3.0 2B2.63B2.1 GB4435.8
gemma-2-2b2.6B2.1 GB4422.9
LFM2 2.6B2.6B2.1 GB4416.3
Granite 3.1 2B2.53B2.0 GB4637.8
Granite 3.3 2B2.53B2.0 GB4620.5
Gemma 1 2B2.51B2.0 GB4620.2
CodeGemma 2B2.51B2.0 GB4622.9