FitMyLLM
▸ AMD· GCN 4.0

AMD Radeon Pro 580X

Running LLMs on the Radeon Pro 580X — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
8 GB
ENTRY-LEVEL
BANDWIDTH
219
GB/S
MODELS Q4
201/449
45%
7B Q4 SPEED
~28
GOOD
▸ MODEL COVERAGE @ Q445% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~65
TOK/S
7B
~28
TOK/S
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
8 GB
BANDWIDTH
219 GB/s
FP16 COMPUTE
5.5 TFLOPS
TDP
185W
MEMORY
GDDR5
ARCHITECTURE
GCN 4.0
STREAM PROCESSORS
2,304
COMPUTE UNITS
36
115
FAST MODELS · >30 TOK/S
Real-time chat speed
201
USABLE · >10 TOK/S
Comfortable for all tasks
201
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Radeon Pro 580X (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 201
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2163
TOK/S · 7% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1442
TOK/S · 7% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1390
TOK/S · 7% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1390
TOK/S · 7% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
760
TOK/S · 8% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
721
TOK/S · 8% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
638
TOK/S · 8% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
581
TOK/S · 9% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
581
TOK/S · 9% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
581
TOK/S · 9% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
556
TOK/S · 9% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
541
TOK/S · 9% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
512
TOK/S · 9% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
389
TOK/S · 10% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
389
TOK/S · 10% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
374
TOK/S · 10% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
343
TOK/S · 10% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
343
TOK/S · 10% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
324
TOK/S · 11% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
324
TOK/S · 11% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
324
TOK/S · 11% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
314
TOK/S · 11% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
263
TOK/S · 12% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
260
TOK/S · 12% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
240
TOK/S · 12% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
224
TOK/S · 13% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
207
TOK/S · 13% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
180
TOK/S · 14% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
177
TOK/S · 15% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
162
TOK/S · 15% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
162
TOK/S · 15% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
162
TOK/S · 15% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
162
TOK/S · 15% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
150
TOK/S · 16% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
150
TOK/S · 16% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
150
TOK/S · 16% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
144
TOK/S · 16% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
137
TOK/S · 17% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
137
TOK/S · 17% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
130
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Radeon Pro 580X.

WHAT THIS CARD IS WORTH

Radeon Pro 580X holds 201 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Falcon3-10B 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
Falcon3-10B10.3B · IQ4_XS7.1 GB20 tok/sEST
Bamba 9B v29.78B · Q4_K_M7.2 GB19 tok/sEST
RecurrentGemma 9B9.63B · Q4_K_M6.8 GB20 tok/sEST
glm-4-9b9.4B · Q4_K_M6.6 GB20 tok/sEST
Yi 1.5 9B9B · Q4_K_M6.5 GB21 tok/sEST
Yi Coder 9B9B · Q4_K_M6.5 GB21 tok/sEST
Ministral 3 8B8.92B · Q4_K_M6.6 GB21 tok/sEST
Ministral 3 8B Reasoning8.92B · Q4_K_M6.6 GB21 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
Radeon Pro 580X8 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

AMD Radeon Pro 580X — 8 GB VRAM.

▸ RADEON PRO 580X SPEC
BRAND
AMD
VRAM
8 GB GDDR5
BANDWIDTH
219 GB/s
FP16 COMPUTE
5.5 TFLOPS
FP32 COMPUTE
5.5 TFLOPS
STREAM PROCESSORS
2,304
TDP
185 W
ARCHITECTURE
GCN 4.0
▸ AI CAPABILITY
201/ 449 models @ Q4

With 8 GB VRAM and 219 GB/s bandwidth, this GPU handles models up to 9.63B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~25 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RADEON PRO 580X
201 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
RecurrentGemma 9B9.63B6.4 GB2035.0
glm-4-9b9.4B6.2 GB2120.5
gemma-2-9b9.2B6.1 GB2130.2
Yi 1.5 9B9B6.0 GB2230.3
Yi Coder 9B9B6.0 GB2235.8
Ministral 3 8B8.92B5.9 GB2225.7
Ministral 3 8B Reasoning8.92B5.9 GB22—
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB2244.2
InternLM3 8B Instruct8.8B5.9 GB2238.7
Gemma 1 7B8.54B5.7 GB2324.7
CodeGemma 7B8.54B5.7 GB2340.2
LFM2 8B A1B8.3B5.6 GB13024.3
Seed-Coder 8B Instruct8.25B5.5 GB2434.1
Seed-Coder 8B Reasoning8.25B5.5 GB2432.9
DeepSeek R1-0528 Qwen3 8B8.2B5.5 GB2436.3
Qwen3-8B8.2B5.5 GB2443.3
Granite 3.0 8B8.17B5.5 GB2436.4
Granite 3.1 8B8.17B5.5 GB2438.6
Command-R7B8.03B5.4 GB2435.3
Aya Expanse 8B8B5.4 GB2427.8