FitMyLLM
▸ NVIDIA· ADA LOVELACE

NVIDIA RTX 4000 SFF Ada Generation

Running LLMs on the RTX 4000 SFF Ada Generation — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $1,250 buys

VRAM
20 GB
MID-RANGE
BANDWIDTH
280
GB/S
MODELS Q4
276/449
61%
7B Q4 SPEED
~32
FAST
▸ MODEL COVERAGE @ Q461% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~75
TOK/S
7B
~32
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
20 GB
BANDWIDTH
280 GB/s
FP16 COMPUTE
19.2 TFLOPS
TDP
70W
MEMORY
GDDR6
ARCHITECTURE
Ada Lovelace
CUDA CORES
6,144
TENSOR CORES
192
PCIE
Gen 4 x16
MSRP
$1,250
162
FAST MODELS · >30 TOK/S
Real-time chat speed
271
USABLE · >10 TOK/S
Comfortable for all tasks
276
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

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▸ COMPATIBLE MODELS· 276
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2489
TOK/S · 3% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1659
TOK/S · 3% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1600
TOK/S · 3% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1600
TOK/S · 3% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
875
TOK/S · 3% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
830
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
734
TOK/S · 3% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
669
TOK/S · 3% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
669
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
669
TOK/S · 3% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
640
TOK/S · 4% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
622
TOK/S · 4% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
589
TOK/S · 4% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
448
TOK/S · 4% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
448
TOK/S · 4% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
431
TOK/S · 4% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
394
TOK/S · 4% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
394
TOK/S · 4% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
373
TOK/S · 4% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
373
TOK/S · 4% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
373
TOK/S · 4% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
361
TOK/S · 4% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
303
TOK/S · 5% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
299
TOK/S · 5% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
277
TOK/S · 5% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
257
TOK/S · 5% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
238
TOK/S · 5% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
207
TOK/S · 6% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
204
TOK/S · 6% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
187
TOK/S · 6% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
187
TOK/S · 6% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
187
TOK/S · 6% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
187
TOK/S · 6% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
172
TOK/S · 6% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
172
TOK/S · 6% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
172
TOK/S · 6% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
166
TOK/S · 7% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
158
TOK/S · 7% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
158
TOK/S · 7% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
149
TOK/S · 28% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 4000 SFF Ada Generation.

WHAT THIS CARD IS WORTH

RTX 4000 SFF Ada Generation holds 276 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Qwen3.5-27B at IQ4_XS.

TOKENS/SEC PER $100
2.7
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
1.60 GB
what memory costs on this card
THE BIGGEST IT TAKES
Qwen3.5-27B27.8B · IQ4_XS17.6 GB10 tok/sEST
gemma-3-27b27.4B · IQ4_XS17.8 GB10 tok/sEST
Qwen 3.6 27B27B · IQ4_XS17.8 GB10 tok/sEST
Gemma 4 26B A4B26B · IQ4_XS17.3 GB70 tok/sEST
Aria 25B A3.9B25.3B · IQ4_XS17.7 GB71 tok/sEST
Mistral-Small-24B24B · Q4_K_M17.1 GB11 tok/sEST
Mistral-Small-3.1-24B24B · Q4_K_M17.8 GB11 tok/sEST
Magistral Small 24B24B · Q4_K_M17.1 GB11 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Max (36GB)27 GB$2,4991.5 tok/s per $100
M3 Pro (36GB)27 GB$1,9991.2 tok/s per $100
M1 Max (32GB)21 GB$1,4993.0 tok/s per $100
M2 Max (32GB)21 GB$1,7992.5 tok/s per $100
RTX 4000 SFF Ada Generation20 GB$1,2502.7 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 RTX 4000 SFF Ada Generation — 20 GB VRAM.

▸ RTX 4000 SFF ADA GENERATION SPEC
BRAND
NVIDIA
VRAM
20 GB GDDR6
BANDWIDTH
280 GB/s
FP16 COMPUTE
19.2 TFLOPS
FP32 COMPUTE
19.2 TFLOPS
CUDA CORES
6,144
TENSOR CORES
192
TDP
70 W
ARCHITECTURE
Ada Lovelace
MSRP
$1250
▸ AI CAPABILITY
276/ 449 models @ Q4

With 20 GB VRAM and 280 GB/s bandwidth, this GPU handles models up to 24B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~32 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 4000 SFF ADA GENERATION
276 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Mistral-Small-24B24B15.2 GB925.0
Mistral-Small-3.1-24B24B15.2 GB928.8
Magistral Small 24B24B15.2 GB947.0
Devstral Small 2 24B24B15.2 GB933.4
Mistral-Small-3.2-24B24B15.2 GB944.4
LFM2 24B A2B24B15.2 GB11219.1
Devstral Small 22B23.57B14.9 GB1035.5
Codestral 22B22.2B14.1 GB1050.1
Mistral Small 22B22.2B14.1 GB1035.2
SOLAR-Pro 22B22.1B14.0 GB1044.2
ERNIE 4.5 21B A3B21.95B13.9 GB75—
GPT-OSS 20B21B13.3 GB6252.9
Reka Flash 321B13.3 GB1136.2
Reka Flash 3.121B13.3 GB1133.4
InternLM2 20B19.8B12.6 GB1145.1
InternLM2.5 20B19.8B12.6 GB1150.9
Ling-lite 16.8B16.8B10.8 GB93—
DeepSeek V2 Lite 16B16B10.3 GB9338.0
StarCoder2 15B15.96B10.2 GB1426.5
DeepSeek-Coder-V2-Lite 15.7B15.7B10.1 GB9343.0