FitMyLLM
▸ NVIDIA· KEPLER

NVIDIA Tesla K20X

Running LLMs on the Tesla K20X — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
6 GB
BUDGET
BANDWIDTH
250
GB/S
MODELS Q4
130/449
29%
7B Q4 SPEED
~29
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
~67
TOK/S
7B
~29
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
250 GB/s
FP16 COMPUTE
3.9 TFLOPS
TDP
235W
MEMORY
GDDR5
ARCHITECTURE
Kepler
CUDA CORES
2,688
PCIE
Gen 3 x16
118
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 K20X 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
2222
TOK/S · 9% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1481
TOK/S · 10% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1429
TOK/S · 10% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1429
TOK/S · 10% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
781
TOK/S · 11% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
741
TOK/S · 11% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
656
TOK/S · 11% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
597
TOK/S · 12% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
597
TOK/S · 12% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
597
TOK/S · 12% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
571
TOK/S · 12% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
556
TOK/S · 12% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
526
TOK/S · 12% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
400
TOK/S · 13% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
400
TOK/S · 13% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
385
TOK/S · 13% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
352
TOK/S · 14% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
352
TOK/S · 14% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
333
TOK/S · 14% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
333
TOK/S · 14% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
333
TOK/S · 14% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
323
TOK/S · 14% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
270
TOK/S · 16% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
267
TOK/S · 16% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
247
TOK/S · 16% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
230
TOK/S · 17% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
213
TOK/S · 18% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
185
TOK/S · 19% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
182
TOK/S · 19% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
167
TOK/S · 20% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
167
TOK/S · 20% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
167
TOK/S · 20% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
167
TOK/S · 20% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
154
TOK/S · 21% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
154
TOK/S · 21% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
154
TOK/S · 21% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
148
TOK/S · 22% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
141
TOK/S · 23% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
141
TOK/S · 23% VRAM
›
S
granite-4.0-h-tiny 6.9B6.9BMoE
GRANITE·128K CTX· CHAT
133
TOK/S · 78% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla K20X.

WHAT THIS CARD IS WORTH

Tesla K20X 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 GB31 tok/sEST
Falcon-H1R 7B7.59B · Q4_K_M5.4 GB31 tok/sEST
Falcon Mamba 7B7.27B · Q4_K_M5.0 GB32 tok/sEST
WizardLM 2 7B7B · Q4_K_M5.3 GB33 tok/sEST
StarCoder2 7B7B · Q4_K_M5.0 GB33 tok/sEST
Dolly v2 7B6.9B · Q4_K_M5.3 GB34 tok/sEST
granite-4.0-h-tiny 6.9B6.9B · Q4_K_M5.0 GB155 tok/sEST
ChatGLM2 6B6.24B · Q5_K_M5.1 GB32 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 K20X6 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 K20X — 6 GB VRAM.

▸ TESLA K20X SPEC
BRAND
NVIDIA
VRAM
6 GB GDDR5
BANDWIDTH
250 GB/s
FP16 COMPUTE
3.9 TFLOPS
FP32 COMPUTE
3.9 TFLOPS
CUDA CORES
2,688
TDP
235 W
ARCHITECTURE
Kepler
▸ AI CAPABILITY
130/ 449 models @ Q4

With 6 GB VRAM and 250 GB/s bandwidth, this GPU handles models up to 7B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~29 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA K20X
130 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Alpaca 7B7B4.8 GB2927.7
Baichuan2 7B7B4.8 GB2921.5
Vicuna 7B7B4.8 GB2922.0
MPT-7B7B4.8 GB297.8
Orca 2 7B7B4.8 GB2926.1
WizardLM 2 7B7B4.8 GB2926.1
StarCoder2 7B7B4.8 GB2917.0
WizardCoder Python 7B7B4.8 GB2953.7
WizardLM 7B7B4.8 GB2915.5
OLMo 3.1 RLZero 7B Code7B4.8 GB2921.8
OLMo 3.1 RLZero 7B Math7B4.8 GB2921.8
Dolly v2 7B6.9B4.7 GB297.0
granite-4.0-h-tiny 6.9B6.9B4.7 GB13349.2
Llama 2 7B6.74B4.6 GB3021.1
CodeLlama 7B6.74B4.6 GB3028.1
LLaMA 1 7B6.74B4.6 GB3030.8
DeepSeek Coder 6.7B6.7B4.6 GB3023.6
OPT 6.7B6.7B4.6 GB3018.5
ChatGLM2 6B6.24B4.3 GB3220.7
ChatGLM3 6B6.24B4.3 GB3242.7