FitMyLLM
▸ NVIDIA· BLACKWELL

NVIDIA RTX 5090

Running LLMs on the RTX 5090 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $1,999 buys

VRAM
32 GB
HIGH-END
BANDWIDTH
1792
GB/S
MODELS Q4
337/449
75%
7B Q4 SPEED
~205
BLAZING
▸ MODEL COVERAGE @ Q475% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~478
TOK/S
7B
~205
TOK/S
14B
~102
TOK/S
32B
~45
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
32 GB
BANDWIDTH
1792 GB/s
FP16 COMPUTE
209 TFLOPS
TDP
575W
MEMORY
GDDR7
ARCHITECTURE
Blackwell
CUDA CORES
21,760
TENSOR CORES
680
PCIE
Gen 5 x16
MSRP
$1,999
337
FAST MODELS · >30 TOK/S
Real-time chat speed
337
USABLE · >10 TOK/S
Comfortable for all tasks
337
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test RTX 5090 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 337
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
15929
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
10619
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
10240
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
10240
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
5600
TOK/S · 2% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
5310
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
4700
TOK/S · 2% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
4279
TOK/S · 2% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
4279
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
4279
TOK/S · 2% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
4096
TOK/S · 2% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
3982
TOK/S · 2% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
3773
TOK/S · 2% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2867
TOK/S · 2% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2867
TOK/S · 2% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
2757
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2524
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2524
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2389
TOK/S · 3% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2389
TOK/S · 3% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2389
TOK/S · 3% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2312
TOK/S · 3% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1937
TOK/S · 3% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1911
TOK/S · 3% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1770
TOK/S · 3% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1648
TOK/S · 3% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1525
TOK/S · 3% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1327
TOK/S · 4% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1303
TOK/S · 4% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1195
TOK/S · 4% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1195
TOK/S · 4% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1195
TOK/S · 4% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1195
TOK/S · 4% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
1103
TOK/S · 4% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
1103
TOK/S · 4% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
1103
TOK/S · 4% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
1062
TOK/S · 4% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
1010
TOK/S · 4% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
1010
TOK/S · 4% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
956
TOK/S · 17% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 5090.

WHAT THIS CARD IS WORTH

RTX 5090 holds 337 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Phi-3.5 MoE 42B at Q4_K_M.

TOKENS/SEC PER $100
7.9
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
Phi-3.5 MoE 42B41.9B · Q4_K_M28.6 GB175 tok/sEST
Falcon 40B40B · Q4_K_M27.4 GB29 tok/sEST
InternVL3 38B38B · Q4_K_M26.7 GB30 tok/sEST
Seed-OSS 36B Instruct36B · Q5_K_S28.6 GB28 tok/sEST
c4ai-command-r-v01 35B35B · Q5_K_M28.1 GB28 tok/sEST
Qwen 3.5 35B A3B35B · Q5_K_M27.5 GB331 tok/sEST
Qwen 3.6 35B A3B35B · Q5_K_M27.5 GB331 tok/sEST
Nous Capybara 34B34.4B · Q5_K_M27.9 GB29 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Max (48GB)36 GB$2,4992.2 tok/s per $100
M3 Max (48GB)36 GB$2,8991.6 tok/s per $100
M4 Pro (48GB)36 GB$1,7991.9 tok/s per $100
A100 SXM4 40 GB40 GB$10,0001.4 tok/s per $100
RTX 509032 GB$1,9997.9 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 5090 — 32 GB VRAM.

▸ RTX 5090 SPEC
BRAND
NVIDIA
VRAM
32 GB GDDR7
BANDWIDTH
1792 GB/s
FP16 COMPUTE
209 TFLOPS
FP32 COMPUTE
104 TFLOPS
CUDA CORES
21,760
TENSOR CORES
680
TDP
575 W
ARCHITECTURE
Blackwell
MSRP
$1999
▸ AI CAPABILITY
337/ 449 models @ Q4

With 32 GB VRAM and 1792 GB/s bandwidth, this GPU handles models up to 41.9B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~205 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 5090
337 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Phi-3.5 MoE 42B41.9B26.1 GB21756.7
Falcon 40B40B24.9 GB3620.9
InternVL3 38B38B23.7 GB3878.9
Seed-OSS 36B Instruct36B22.5 GB4054.4
c4ai-command-r-v01 35B35B21.9 GB4127.5
Qwen 3.5 35B A3B35B21.9 GB47853.3
Qwen 3.6 35B A3B35B21.9 GB47853.9
Nous Capybara 34B34.4B21.5 GB4242.0
Yi-1.5 34B34.4B21.5 GB4245.3
Falcon-H1 34B34B21.3 GB4266.1
CodeLlama 34B34B21.3 GB4225.4
Nous Hermes 2 34B34B21.3 GB4247.0
Phind CodeLlama 34B34B21.3 GB4268.1
LLaVA-1.6 Yi 34B34B21.3 GB4247.4
WizardCoder Python 34B34B21.3 GB4273.2
Yi 34B34B21.3 GB4233.4
Qwen3-VL 32B Instruct33.36B20.9 GB4344.6
DeepSeek Coder 33B33B20.7 GB4326.0
Vicuna 33B33B20.7 GB4317.2
LLaMA 1 30B33B20.7 GB4317.8