FitMyLLM
NVIDIA· ADA LOVELACE

NVIDIA L40 48GB

Running LLMs on the L40 48GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $5,500 buys

VRAM
48 GB
FLAGSHIP
BANDWIDTH
864
GB/S
MODELS Q4
345/449
77%
7B Q4 SPEED
~99
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
~230
TOK/S
7B
~99
TOK/S
14B
~49
TOK/S
32B
~22
TOK/S
70B
~10
TOK/S
▸ 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
48 GB
BANDWIDTH
864 GB/s
FP16 COMPUTE
181 TFLOPS
TDP
300W
MEMORY
GDDR6
ARCHITECTURE
Ada Lovelace
CUDA CORES
18,176
TENSOR CORES
568
PCIE
Gen 4 x16
MSRP
$5,500
291
FAST MODELS · >30 TOK/S
Real-time chat speed
345
USABLE · >10 TOK/S
Comfortable for all tasks
345
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying L40 48GB 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· 345
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
7680
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5120
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
4937
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
4937
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2700
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2560
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2266
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2063
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2063
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2063
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1975
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1920
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1819
TOK/S · 2% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1382
TOK/S · 2% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1382
TOK/S · 2% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1329
TOK/S · 2% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1217
TOK/S · 2% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1217
TOK/S · 2% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1152
TOK/S · 2% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1152
TOK/S · 2% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1152
TOK/S · 2% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1115
TOK/S · 2% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
934
TOK/S · 2% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
922
TOK/S · 2% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
853
TOK/S · 2% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
794
TOK/S · 2% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
735
TOK/S · 2% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
640
TOK/S · 2% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
628
TOK/S · 2% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
576
TOK/S · 3% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
576
TOK/S · 3% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
576
TOK/S · 3% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
576
TOK/S · 3% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
532
TOK/S · 3% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
532
TOK/S · 3% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
532
TOK/S · 3% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
512
TOK/S · 3% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
487
TOK/S · 3% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
487
TOK/S · 3% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
461
TOK/S · 12% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your L40 48GB.

WHAT THIS CARD IS WORTH

L40 48GB holds 345 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is DeepSeek R1 Distill Llama 70B at IQ4_XS.

TOKENS/SEC PER $100
1.5
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.87 GB
what memory costs on this card
THE BIGGEST IT TAKES
DeepSeek R1 Distill Llama 70B70.6B · IQ4_XS43.1 GB10 tok/sEST
Llama 3.3 70B70.6B · IQ4_XS43.1 GB10 tok/sEST
Llama 3.1 70B70.6B · IQ4_XS43.1 GB10 tok/sEST
Llama 3 70B70.6B · IQ4_XS43.1 GB10 tok/sEST
Llama-3.1-Nemotron-70B70.6B · IQ4_XS43.1 GB10 tok/sEST
Cogito 70B70B · IQ4_XS42.8 GB10 tok/sEST
Llama 2 70B70B · IQ4_XS42.8 GB10 tok/sEST
CodeLlama 70B70B · IQ4_XS42.8 GB10 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
H100 SXM5 64 GB64 GB$25,0000.2 tok/s per $100
Radeon Instinct MI20064 GB$10,0001.8 tok/s per $100
Radeon Instinct MI21064 GB$8,0002.2 tok/s per $100
M1 Ultra (64GB)48 GB$2,4992.9 tok/s per $100
L40 48GB48 GB$5,5001.5 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 L40 48GB — 48 GB VRAM.

L40 48GB SPEC
BRAND
NVIDIA
VRAM
48 GB GDDR6
BANDWIDTH
864 GB/s
FP16 COMPUTE
181 TFLOPS
FP32 COMPUTE
90.5 TFLOPS
CUDA CORES
18,176
TENSOR CORES
568
TDP
300 W
ARCHITECTURE
Ada Lovelace
MSRP
$5500
▸ AI CAPABILITY
345/ 449 models @ Q4

With 48 GB VRAM and 864 GB/s bandwidth, this GPU handles models up to 65.2B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~99 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR L40 48GB
345 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
LLaMA 1 65B65.2B40.3 GB1142.6
Jamba 2 Mini52B32.3 GB589.2
Jamba 1.5 Mini 52B51.6B32.0 GB5824.2
Kimi-Linear-48B-A3B48B29.8 GB23026.6
Nemotron-H 47B47B29.2 GB1584.6
Mixtral-8x7B46.7B29.0 GB5318.8
Nous-Hermes-2-Mixtral-8x7B-DPO46.7B29.0 GB5327.4
Dolphin 2.6 Mixtral 8x7B46.7B29.0 GB5323.8
Phi-3.5 MoE 42B41.9B26.1 GB10556.7
Falcon 40B40B24.9 GB1720.9
InternVL3 38B38B23.7 GB1878.9
Seed-OSS 36B Instruct36B22.5 GB1954.4
c4ai-command-r-v01 35B35B21.9 GB2027.5
Qwen 3.5 35B A3B35B21.9 GB23053.3
Qwen 3.6 35B A3B35B21.9 GB23053.9
Nous Capybara 34B34.4B21.5 GB2042.0
Yi-1.5 34B34.4B21.5 GB2045.3
Falcon-H1 34B34B21.3 GB2066.1
CodeLlama 34B34B21.3 GB2025.4
Nous Hermes 2 34B34B21.3 GB2047.0