FitMyLLM
▸ NVIDIA· AMPERE

NVIDIA PG506-232

Running LLMs on the PG506-232 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
24 GB
HIGH-END
BANDWIDTH
933
GB/S
MODELS Q4
293/449
65%
7B Q4 SPEED
~107
BLAZING
▸ MODEL COVERAGE @ Q465% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~249
TOK/S
7B
~107
TOK/S
14B
~53
TOK/S
32B
~23
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
24 GB
BANDWIDTH
933 GB/s
FP16 COMPUTE
10.3 TFLOPS
TDP
165W
MEMORY
HBM2
ARCHITECTURE
Ampere
CUDA CORES
3,584
TENSOR CORES
224
PCIE
Gen 4 x16
283
FAST MODELS · >30 TOK/S
Real-time chat speed
293
USABLE · >10 TOK/S
Comfortable for all tasks
293
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test PG506-232 (or anything bigger) without committing. Pay by the second, cancel anytime.

Rent on Vast.ai →Or RunPod →

Spin up in ~60s. Pay by the second. Cancel anytime.

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▸ COMPATIBLE MODELS· 293
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
8293
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5529
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
5331
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
5331
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2916
TOK/S · 3% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2764
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2447
TOK/S · 3% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2228
TOK/S · 3% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2228
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2228
TOK/S · 3% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2133
TOK/S · 3% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
2073
TOK/S · 3% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1964
TOK/S · 3% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1493
TOK/S · 3% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1493
TOK/S · 3% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1435
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1314
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1314
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1244
TOK/S · 4% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1244
TOK/S · 4% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1244
TOK/S · 4% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1204
TOK/S · 4% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1009
TOK/S · 4% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
995
TOK/S · 4% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
921
TOK/S · 4% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
858
TOK/S · 4% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
794
TOK/S · 4% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
691
TOK/S · 5% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
679
TOK/S · 5% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
622
TOK/S · 5% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
622
TOK/S · 5% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
622
TOK/S · 5% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
622
TOK/S · 5% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
574
TOK/S · 5% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
574
TOK/S · 5% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
574
TOK/S · 5% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
553
TOK/S · 5% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
526
TOK/S · 6% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
526
TOK/S · 6% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
498
TOK/S · 23% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your PG506-232.

▸Find models for PG506-232Compare with other GPUs
WHAT THIS CARD IS WORTH

PG506-232 holds 293 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is DeepSeek Coder 33B at IQ4_XS.

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
DeepSeek Coder 33B33B · IQ4_XS21.6 GB22 tok/sEST
DeepSeek-R1-Distill-Qwen-32B32.8B · IQ4_XS21.5 GB22 tok/sEST
Qwen3 32B32.8B · IQ4_XS21.5 GB22 tok/sEST
Qwen2.5-32B32.5B · IQ4_XS21.3 GB22 tok/sEST
Qwen 2.5 Coder 32B32.5B · IQ4_XS21.3 GB22 tok/sEST
QwQ-32B32.5B · IQ4_XS21.3 GB22 tok/sEST
Granite Switch 4.1 30B Preview32.24B · IQ4_XS21.1 GB23 tok/sEST
OLMo-2-0325-32B32.2B · IQ4_XS21.1 GB23 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
RTX 509032 GB$1,9997.9 tok/s per $100
RTX 5090 D32 GB$1,9997.9 tok/s per $100
PG506-23224 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 PG506-232 — 24 GB VRAM.

▸ PG506-232 SPEC
BRAND
NVIDIA
VRAM
24 GB HBM2
BANDWIDTH
933 GB/s
FP16 COMPUTE
10.3 TFLOPS
FP32 COMPUTE
10.3 TFLOPS
CUDA CORES
3,584
TENSOR CORES
224
TDP
165 W
ARCHITECTURE
Ampere
▸ AI CAPABILITY
293/ 449 models @ Q4

With 24 GB VRAM and 933 GB/s bandwidth, this GPU handles models up to 30.5B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~107 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR PG506-232
293 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Qwen3 30B A3B30.5B19.1 GB24946.7
Qwen3-Coder 30B-A3B30.5B19.1 GB22636.9
Qwen3-30B-A3B Instruct 250730.5B19.1 GB22643.0
MPT-30B30B18.8 GB2526.8
OPT 30B30B18.8 GB256.3
Qwen3-Omni 30B-A3B30B18.8 GB24940.6
Granite 4.1 30B30B18.8 GB2524.7
TranslateGemma 27B28.84B18.1 GB2638.6
PaliGemma 2 28B28B17.6 GB2738.6
ERNIE 4.5 VL 28B A3B Thinking28B17.6 GB249—
Qwen3.5-27B27.8B17.5 GB2759.4
Qwen 3.8 27B27.78B17.5 GB2764.6
gemma-3-27b27.4B17.2 GB2727.2
gemma-2-27b27.2B17.1 GB2734.6
Qwen 3.6 27B27B17.0 GB2841.1
Gemma 4 26B A4B26B16.4 GB18747.9
Aria 25B A3.9B25.3B16.0 GB19164.8
Mistral-Small-24B24B15.2 GB3125.0
Mistral-Small-3.1-24B24B15.2 GB3128.8
Magistral Small 24B24B15.2 GB3147.0