FitMyLLM
▸ NVIDIA· KEPLER

NVIDIA Tesla K20s

Running LLMs on the Tesla K20s — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
5 GB
BUDGET
BANDWIDTH
208
GB/S
MODELS Q4
104/449
23%
7B Q4 SPEED
~24
GOOD
▸ MODEL COVERAGE @ Q423% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~55
TOK/S
7B
~24
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
5 GB
BANDWIDTH
208 GB/s
FP16 COMPUTE
2.9 TFLOPS
TDP
225W
MEMORY
GDDR5
ARCHITECTURE
Kepler
CUDA CORES
2,496
PCIE
Gen 2 x16
104
FAST MODELS · >30 TOK/S
Real-time chat speed
104
USABLE · >10 TOK/S
Comfortable for all tasks
104
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Tesla K20s 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· 104
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1849
TOK/S · 11% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1233
TOK/S · 11% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1189
TOK/S · 11% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1189
TOK/S · 11% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
650
TOK/S · 13% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
616
TOK/S · 13% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
546
TOK/S · 13% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
497
TOK/S · 14% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
497
TOK/S · 14% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
497
TOK/S · 14% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
475
TOK/S · 14% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
462
TOK/S · 14% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
438
TOK/S · 14% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
333
TOK/S · 16% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
333
TOK/S · 16% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
320
TOK/S · 16% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
293
TOK/S · 17% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
293
TOK/S · 17% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
277
TOK/S · 17% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
277
TOK/S · 17% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
277
TOK/S · 17% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
268
TOK/S · 17% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
225
TOK/S · 19% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
222
TOK/S · 19% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
205
TOK/S · 20% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
191
TOK/S · 20% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
177
TOK/S · 21% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
154
TOK/S · 23% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
151
TOK/S · 23% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
139
TOK/S · 24% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
139
TOK/S · 24% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
139
TOK/S · 24% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
139
TOK/S · 24% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
128
TOK/S · 26% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
128
TOK/S · 26% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
128
TOK/S · 26% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
123
TOK/S · 26% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
117
TOK/S · 27% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
117
TOK/S · 27% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
111
TOK/S · 28% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla K20s.

WHAT THIS CARD IS WORTH

Tesla K20s holds 104 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is ChatGLM2 6B 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
ChatGLM2 6B6.24B · Q4_K_M4.4 GB33 tok/sEST
ChatGLM3 6B6.24B · Q4_K_M4.4 GB33 tok/sEST
Yi-1.5 6B6.06B · Q4_K_M4.4 GB33 tok/sEST
Yi 6B6B · Q4_K_M4.4 GB34 tok/sEST
Gemma 4 E2B5.1B · Q4_K_M4.4 GB40 tok/sEST
Qwen 3.5 4B4.66B · Q4_K_M4.1 GB44 tok/sEST
InternLM2 5B4.5B · Q5_K_M4.1 GB39 tok/sEST
Qwen3-VL 4B Instruct4.44B · Q4_K_M4.4 GB46 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3070 Ti8 GB$49912.6 tok/s per $100
RTX 3060 Ti GDDR6X8 GB$39915.8 tok/s per $100
RTX 3070 Ti 8 GB GA1028 GB$59910.5 tok/s per $100
Arc A7508 GB$19910.1 tok/s per $100
Tesla K20s5 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 K20s — 5 GB VRAM.

▸ TESLA K20S SPEC
BRAND
NVIDIA
VRAM
5 GB GDDR5
BANDWIDTH
208 GB/s
FP16 COMPUTE
2.9 TFLOPS
FP32 COMPUTE
2.9 TFLOPS
CUDA CORES
2,496
TDP
225 W
ARCHITECTURE
Kepler
▸ AI CAPABILITY
104/ 449 models @ Q4

With 5 GB VRAM and 208 GB/s bandwidth, this GPU handles models up to 4.5B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~24 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA K20S
104 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
InternLM2 5B4.5B3.2 GB3747.6
Qwen3-VL 4B Instruct4.44B3.2 GB3726.6
MedGemma 1.5 4B4.3B3.1 GB395.5
Gemma 3 4B4.3B3.1 GB3922.8
Ministral 3 3B Reasoning4.25B3.1 GB39—
Qwen 1.5 4B4B2.9 GB4212.6
Qwen3 4B4B2.9 GB4240.7
Qwen3-4B Instruct 25074B2.9 GB4237.2
Qwen3-Embedding 4B4B2.9 GB42—
Granite Vision 4.1 4B4B2.9 GB42—
Nemotron 3 Nano 4B3.97B2.9 GB4232.0
Ministral 3 3B3.85B2.8 GB4321.4
Phi-3.5 Mini 3.8B3.82B2.8 GB4446.6
phi-3-mini-4k 3.8B3.8B2.8 GB4430.5
Phi-4-mini 3.8B3.8B2.8 GB4449.0
Qwen2.5-VL-3B3.8B2.8 GB4429.9
Jina Embeddings v43.8B2.8 GB44—
Cogito 3B3.61B2.7 GB4622.1
Falcon3-3B3.23B2.5 GB5225.7
granite-4.0-h-micro 3.2B3.2B2.4 GB5218.4