FitMyLLM
▸ NVIDIA· ADA LOVELACE

NVIDIA RTX 5000 Ada Generation

Running LLMs on the RTX 5000 Ada Generation — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $4,000 buys

VRAM
32 GB
HIGH-END
BANDWIDTH
576
GB/S
MODELS Q4
337/449
75%
7B Q4 SPEED
~66
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
~154
TOK/S
7B
~66
TOK/S
14B
~33
TOK/S
32B
~14
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
576 GB/s
FP16 COMPUTE
65.3 TFLOPS
TDP
250W
MEMORY
GDDR6
ARCHITECTURE
Ada Lovelace
CUDA CORES
12,800
TENSOR CORES
400
PCIE
Gen 4 x16
MSRP
$4,000
277
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?

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▸ COMPATIBLE MODELS· 337
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
5120
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
3413
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
3291
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
3291
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1800
TOK/S · 2% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1707
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1511
TOK/S · 2% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
1376
TOK/S · 2% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
1376
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
1376
TOK/S · 2% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1317
TOK/S · 2% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1280
TOK/S · 2% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1213
TOK/S · 2% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
922
TOK/S · 2% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
922
TOK/S · 2% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
886
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
811
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
811
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
768
TOK/S · 3% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
768
TOK/S · 3% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
768
TOK/S · 3% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
743
TOK/S · 3% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
623
TOK/S · 3% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
614
TOK/S · 3% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
569
TOK/S · 3% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
530
TOK/S · 3% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
490
TOK/S · 3% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
427
TOK/S · 4% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
419
TOK/S · 4% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
384
TOK/S · 4% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
384
TOK/S · 4% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
384
TOK/S · 4% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
384
TOK/S · 4% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
354
TOK/S · 4% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
354
TOK/S · 4% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
354
TOK/S · 4% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
341
TOK/S · 4% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
325
TOK/S · 4% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
325
TOK/S · 4% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
307
TOK/S · 17% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 5000 Ada Generation.

WHAT THIS CARD IS WORTH

RTX 5000 Ada Generation 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
1.5
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.80 GB
what memory costs on this card
THE BIGGEST IT TAKES
Phi-3.5 MoE 42B41.9B · Q4_K_M28.6 GB68 tok/sEST
Falcon 40B40B · Q4_K_M27.4 GB11 tok/sEST
InternVL3 38B38B · Q4_K_M26.7 GB12 tok/sEST
Seed-OSS 36B Instruct36B · Q5_K_S28.6 GB11 tok/sEST
c4ai-command-r-v01 35B35B · Q5_K_M28.1 GB11 tok/sEST
Qwen 3.5 35B A3B35B · Q5_K_M27.5 GB128 tok/sEST
Qwen 3.6 35B A3B35B · Q5_K_M27.5 GB128 tok/sEST
Nous Capybara 34B34.4B · Q5_K_M27.9 GB11 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 5000 Ada Generation32 GB$4,0001.5 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 RTX 5000 Ada Generation — 32 GB VRAM.

▸ RTX 5000 ADA GENERATION SPEC
BRAND
NVIDIA
VRAM
32 GB GDDR6
BANDWIDTH
576 GB/s
FP16 COMPUTE
65.3 TFLOPS
FP32 COMPUTE
65.3 TFLOPS
CUDA CORES
12,800
TENSOR CORES
400
TDP
250 W
ARCHITECTURE
Ada Lovelace
MSRP
$4000
▸ AI CAPABILITY
337/ 449 models @ Q4

With 32 GB VRAM and 576 GB/s bandwidth, this GPU handles models up to 41.9B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~66 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 5000 ADA GENERATION
337 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Phi-3.5 MoE 42B41.9B26.1 GB7056.7
Falcon 40B40B24.9 GB1220.9
InternVL3 38B38B23.7 GB1278.9
Seed-OSS 36B Instruct36B22.5 GB1354.4
c4ai-command-r-v01 35B35B21.9 GB1327.5
Qwen 3.5 35B A3B35B21.9 GB15453.3
Qwen 3.6 35B A3B35B21.9 GB15453.9
Nous Capybara 34B34.4B21.5 GB1342.0
Yi-1.5 34B34.4B21.5 GB1345.3
Falcon-H1 34B34B21.3 GB1466.1
CodeLlama 34B34B21.3 GB1425.4
Nous Hermes 2 34B34B21.3 GB1447.0
Phind CodeLlama 34B34B21.3 GB1468.1
LLaVA-1.6 Yi 34B34B21.3 GB1447.4
WizardCoder Python 34B34B21.3 GB1473.2
Yi 34B34B21.3 GB1433.4
Qwen3-VL 32B Instruct33.36B20.9 GB1444.6
DeepSeek Coder 33B33B20.7 GB1426.0
Vicuna 33B33B20.7 GB1417.2
LLaMA 1 30B33B20.7 GB1417.8