FitMyLLM
NVIDIA· KEPLER

NVIDIA Tesla K40m

VRAM
12 GB
ENTRY-LEVEL
BANDWIDTH
288
GB/S
MODELS Q4
248/449
55%
7B Q4 SPEED
~33
FAST
▸ MODEL COVERAGE @ Q455% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~77
TOK/S
7B
~33
TOK/S
14B
~16
TOK/S
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
12 GB
BANDWIDTH
288 GB/s
FP16 COMPUTE
5 TFLOPS
TDP
245W
MEMORY
GDDR5
ARCHITECTURE
Kepler
CUDA CORES
2,880
PCIE
Gen 3 x16
165
FAST MODELS · >30 TOK/S
Real-time chat speed
248
USABLE · >10 TOK/S
Comfortable for all tasks
248
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying Tesla K40m 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· 248
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2560
TOK/S · 5% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1707
TOK/S · 5% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1646
TOK/S · 5% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1646
TOK/S · 5% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
900
TOK/S · 5% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
853
TOK/S · 5% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
755
TOK/S · 6% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
688
TOK/S · 6% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
688
TOK/S · 6% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
688
TOK/S · 6% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
658
TOK/S · 6% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
640
TOK/S · 6% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
606
TOK/S · 6% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
461
TOK/S · 7% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
461
TOK/S · 7% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
443
TOK/S · 7% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
406
TOK/S · 7% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
406
TOK/S · 7% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
384
TOK/S · 7% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
384
TOK/S · 7% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
384
TOK/S · 7% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
372
TOK/S · 7% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
311
TOK/S · 8% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
307
TOK/S · 8% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
284
TOK/S · 8% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
265
TOK/S · 9% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
245
TOK/S · 9% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
213
TOK/S · 10% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
209
TOK/S · 10% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
192
TOK/S · 10% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
192
TOK/S · 10% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
192
TOK/S · 10% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
192
TOK/S · 10% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
177
TOK/S · 11% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
177
TOK/S · 11% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
177
TOK/S · 11% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
171
TOK/S · 11% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
162
TOK/S · 11% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
162
TOK/S · 11% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
154
TOK/S · 46% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Tesla K40m.

WHAT THIS CARD IS WORTH

Tesla K40m holds 248 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Ling-lite 16.8B at IQ4_XS.

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
Ling-lite 16.8B16.8B · IQ4_XS10.7 GB118 tok/sEST
DeepSeek V2 Lite 16B16B · Q4_K_S10.6 GB113 tok/sEST
StarCoder2 15B15.96B · Q4_K_S10.7 GB17 tok/sEST
DeepSeek-Coder-V2-Lite 15.7B15.7B · Q4_K_S10.4 GB113 tok/sEST
DeepSeek R1 Distill Qwen 14B14.8B · Q4_K_S10.4 GB18 tok/sEST
DeepCoder 14B14.8B · Q4_K_S10.4 GB18 tok/sEST
Qwen2.5-Coder-14B14.8B · Q4_K_S10.4 GB18 tok/sEST
Qwen2.5-14B14.8B · Q4_K_S10.4 GB18 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Pro (24GB)16 GB$1,3992.5 tok/s per $100
M4 (24GB)16 GB$6992.9 tok/s per $100
M2 (24GB)16 GB$9991.7 tok/s per $100
M3 (24GB)16 GB$9991.7 tok/s per $100
Tesla K40m12 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 K40m — 12 GB VRAM.

TESLA K40M SPEC
BRAND
NVIDIA
VRAM
12 GB GDDR5
BANDWIDTH
288 GB/s
FP16 COMPUTE
5 TFLOPS
FP32 COMPUTE
5 TFLOPS
CUDA CORES
2,880
TDP
245 W
ARCHITECTURE
Kepler
▸ AI CAPABILITY
248/ 449 models @ Q4

With 12 GB VRAM and 288 GB/s bandwidth, this GPU handles models up to 14.8B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~33 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR TESLA K40M
248 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
DeepSeek R1 Distill Qwen 14B14.8B9.5 GB1643.9
DeepCoder 14B14.8B9.5 GB1638.7
Qwen2.5-Coder-14B14.8B9.5 GB1641.3
Qwen2.5-14B14.8B9.5 GB1641.3
Qwen3 14B14.8B9.5 GB1645.7
phi-4 14B14.66B9.4 GB1633.7
Phi-4-reasoning 14B14.66B9.4 GB1633.7
Phi-4-reasoning-plus 14B14.66B9.4 GB1675.5
Phi-3-medium-14b14B9.0 GB1633.7
Qwen 1.5 14B14B9.0 GB1641.3
Ministral 3 14B Reasoning13.95B9.0 GB17
Baichuan2 13B13B8.4 GB1823.6
Llama 2 13B13B8.4 GB1817.2
CodeLlama 13B13B8.4 GB1819.7
Vicuna 13B13B8.4 GB1811.8
LLaMA 1 13B13B8.4 GB1832.9
OPT 13B13B8.4 GB1835.8
Orca 2 13B13B8.4 GB1825.4
WizardCoder Python 13B13B8.4 GB1860.1
WizardLM 13B13B8.4 GB1819.5