FitMyLLM
NVIDIA· FERMI

NVIDIA Quadro 4000

Running LLMs on the Quadro 4000 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
89.9
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~10
USABLE
▸ MODEL COVERAGE @ Q410% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
1.7GB NEEDED
7B
3.9GB NEEDED
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
2 GB
BANDWIDTH
89.9 GB/s
FP16 COMPUTE
0.5 TFLOPS
TDP
142W
MEMORY
GDDR5
ARCHITECTURE
Fermi
CUDA CORES
256
PCIE
Gen 2 x16
47
FAST MODELS · >30 TOK/S
Real-time chat speed
47
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Quadro 4000 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
799
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
533
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
514
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
514
TOK/S · 29% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
281
TOK/S · 32% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
266
TOK/S · 33% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
236
TOK/S · 34% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
215
TOK/S · 35% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
215
TOK/S · 35% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
215
TOK/S · 35% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
205
TOK/S · 35% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
200
TOK/S · 35% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
189
TOK/S · 36% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
144
TOK/S · 40% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
144
TOK/S · 40% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
138
TOK/S · 40% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
127
TOK/S · 42% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
127
TOK/S · 42% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
120
TOK/S · 43% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
120
TOK/S · 43% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
120
TOK/S · 43% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
116
TOK/S · 43% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
97
TOK/S · 47% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
96
TOK/S · 47% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
89
TOK/S · 49% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
83
TOK/S · 51% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
77
TOK/S · 53% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
67
TOK/S · 57% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
65
TOK/S · 58% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
60
TOK/S · 61% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
60
TOK/S · 61% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
60
TOK/S · 61% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
60
TOK/S · 61% VRAM
A
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
55
TOK/S · 64% VRAM
A
OPT 1.3B1.3B
OPT·2K CTX· CHAT
55
TOK/S · 64% VRAM
A
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
55
TOK/S · 64% VRAM
A
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
53
TOK/S · 66% VRAM
A
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
51
TOK/S · 68% VRAM
A
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
51
TOK/S · 68% VRAM
A
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
48
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Quadro 4000.

WHAT THIS CARD IS WORTH

Quadro 4000 holds 47 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Moondream2 1.9B 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
Moondream2 1.9B1.9B · Q4_K_M1.7 GB58 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB57 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB59 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB53 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB55 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB55 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB55 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB55 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
Quadro 40002 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 Quadro 4000 — 2 GB VRAM.

QUADRO 4000 SPEC
BRAND
NVIDIA
VRAM
2 GB GDDR5
BANDWIDTH
89.9 GB/s
FP16 COMPUTE
0.5 TFLOPS
FP32 COMPUTE
0.5 TFLOPS
CUDA CORES
256
TDP
142 W
ARCHITECTURE
Fermi
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 89.9 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~10 tok/s.

§ 01TOP MODELS FOR QUADRO 4000
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB455.1
stablelm-2-1_6b1.6B1.5 GB459.5
Falcon-H1 1.5B1.55B1.4 GB4643.8
Qwen2.5-Coder-1.5B1.5B1.4 GB4819.6
Qwen2 Math 1.5B1.5B1.4 GB4819.6
Qwen 2.5 1.5B1.5B1.4 GB4830.2
Yi Coder 1.5B1.5B1.4 GB4814.6
Stella en 1.5B v51.5B1.4 GB48
Phi-1 1.3B1.42B1.4 GB517.2
Phi-1.5 1.3B1.42B1.4 GB517.2
DeepSeek Coder 1.3B1.35B1.3 GB5316.8
EXAONE-4.0-1.2B1.3B1.3 GB5518.9
OPT 1.3B1.3B1.3 GB555.3
MiniCPM-V 4.61.3B1.3 GB5521.5
LFM2.5-1.2B-Thinking1.2B1.2 GB6019.6
Llama-3.2-1B1.2B1.2 GB6010.1
LFM2 1.2B1.2B1.2 GB6015.6
Zamba2 1.2B1.2B1.2 GB6041.5
TinyLlama 1.1B1.1B1.2 GB6513.6
MiniCPM5 1B1.08B1.1 GB6726.5