FitMyLLM
NVIDIA· AMPERE

NVIDIA A100 SXM4 40 GB

Running LLMs on the A100 SXM4 40 GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $10,000 buys

VRAM
40 GB
HIGH-END
BANDWIDTH
1560
GB/S
MODELS Q4
344/449
77%
7B Q4 SPEED
~178
BLAZING
▸ MODEL COVERAGE @ Q477% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~416
TOK/S
7B
~178
TOK/S
14B
~89
TOK/S
32B
~39
TOK/S
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
40 GB
BANDWIDTH
1560 GB/s
FP16 COMPUTE
78 TFLOPS
TDP
400W
MEMORY
HBM2e
ARCHITECTURE
Ampere
CUDA CORES
6,912
TENSOR CORES
432
PCIE
Gen 4 x16
MSRP
$10,000
343
FAST MODELS · >30 TOK/S
Real-time chat speed
344
USABLE · >10 TOK/S
Comfortable for all tasks
344
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying A100 SXM4 40 GB 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· 344
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
13867
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
9244
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
8914
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
8914
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
4875
TOK/S · 2% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
4622
TOK/S · 2% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
4092
TOK/S · 2% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
3725
TOK/S · 2% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
3725
TOK/S · 2% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
3725
TOK/S · 2% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
3566
TOK/S · 2% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
3467
TOK/S · 2% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
3284
TOK/S · 2% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2496
TOK/S · 2% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2496
TOK/S · 2% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
2400
TOK/S · 2% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2197
TOK/S · 2% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2197
TOK/S · 2% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2080
TOK/S · 2% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2080
TOK/S · 2% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2080
TOK/S · 2% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2013
TOK/S · 2% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1686
TOK/S · 2% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1664
TOK/S · 2% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1541
TOK/S · 2% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1434
TOK/S · 3% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1328
TOK/S · 3% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1156
TOK/S · 3% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1135
TOK/S · 3% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1040
TOK/S · 3% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1040
TOK/S · 3% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1040
TOK/S · 3% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1040
TOK/S · 3% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
960
TOK/S · 3% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
960
TOK/S · 3% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
960
TOK/S · 3% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
924
TOK/S · 3% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
879
TOK/S · 3% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
879
TOK/S · 3% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
832
TOK/S · 14% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your A100 SXM4 40 GB.

WHAT THIS CARD IS WORTH

A100 SXM4 40 GB holds 344 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Jamba 2 Mini at Q4_K_M.

TOKENS/SEC PER $100
1.4
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.40 GB
what memory costs on this card
THE BIGGEST IT TAKES
Jamba 2 Mini52B · Q4_K_M34.8 GB85 tok/sEST
Jamba 1.5 Mini 52B51.6B · Q4_K_M34.5 GB85 tok/sEST
Kimi-Linear-48B-A3B48B · Q4_K_M32.0 GB342 tok/sEST
Nemotron-H 47B47B · Q4_K_M32.8 GB22 tok/sEST
Mixtral-8x7B46.7B · Q5_K_S35.5 GB69 tok/sEST
Nous-Hermes-2-Mixtral-8x7B-DPO46.7B · Q5_K_S35.5 GB69 tok/sEST
Dolphin 2.6 Mixtral 8x7B46.7B · Q5_K_S35.5 GB69 tok/sEST
Phi-3.5 MoE 42B41.9B · Q5_K_M32.9 GB133 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M1 Ultra (64GB)48 GB$2,4992.9 tok/s per $100
M2 Ultra (64GB)48 GB$2,9992.4 tok/s per $100
M4 Max (64GB)48 GB$2,8991.9 tok/s per $100
M2 Max (64GB)48 GB$2,2992.0 tok/s per $100
A100 SXM4 40 GB40 GB$10,0001.4 tok/s per $100

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 A100 SXM4 40 GB — 40 GB VRAM.

A100 SXM4 40 GB SPEC
BRAND
NVIDIA
VRAM
40 GB HBM2e
BANDWIDTH
1560 GB/s
FP16 COMPUTE
78 TFLOPS
FP32 COMPUTE
19.5 TFLOPS
CUDA CORES
6,912
TENSOR CORES
432
TDP
400 W
ARCHITECTURE
Ampere
MSRP
$10000
▸ AI CAPABILITY
344/ 449 models @ Q4

With 40 GB VRAM and 1560 GB/s bandwidth, this GPU handles models up to 52B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~178 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR A100 SXM4 40 GB
344 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Jamba 2 Mini52B32.3 GB1049.2
Jamba 1.5 Mini 52B51.6B32.0 GB10424.2
Kimi-Linear-48B-A3B48B29.8 GB41626.6
Nemotron-H 47B47B29.2 GB2784.6
Mixtral-8x7B46.7B29.0 GB9618.8
Nous-Hermes-2-Mixtral-8x7B-DPO46.7B29.0 GB9627.4
Dolphin 2.6 Mixtral 8x7B46.7B29.0 GB9623.8
Phi-3.5 MoE 42B41.9B26.1 GB18956.7
Falcon 40B40B24.9 GB3120.9
InternVL3 38B38B23.7 GB3378.9
Seed-OSS 36B Instruct36B22.5 GB3554.4
c4ai-command-r-v01 35B35B21.9 GB3627.5
Qwen 3.5 35B A3B35B21.9 GB41653.3
Qwen 3.6 35B A3B35B21.9 GB41653.9
Nous Capybara 34B34.4B21.5 GB3642.0
Yi-1.5 34B34.4B21.5 GB3645.3
Falcon-H1 34B34B21.3 GB3766.1
CodeLlama 34B34B21.3 GB3725.4
Nous Hermes 2 34B34B21.3 GB3747.0
Phind CodeLlama 34B34B21.3 GB3768.1