FitMyLLM
AMD· GCN 4.0

AMD Radeon RX 570 Mobile

VRAM
8 GB
ENTRY-LEVEL
BANDWIDTH
211
GB/S
MODELS Q4
201/449
45%
7B Q4 SPEED
~27
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
~63
TOK/S
7B
~27
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
211 GB/s
FP16 COMPUTE
4.9 TFLOPS
TDP
85W
MEMORY
GDDR5
ARCHITECTURE
GCN 4.0
STREAM PROCESSORS
2,048
COMPUTE UNITS
32
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 RX 570 Mobile (or anything bigger) without committing. Pay by the second, cancel anytime.

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

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 201
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2084
TOK/S · 7% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1389
TOK/S · 7% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1340
TOK/S · 7% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1340
TOK/S · 7% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
733
TOK/S · 8% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
695
TOK/S · 8% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
615
TOK/S · 8% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
560
TOK/S · 9% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
560
TOK/S · 9% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
560
TOK/S · 9% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
536
TOK/S · 9% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
521
TOK/S · 9% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
494
TOK/S · 9% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
375
TOK/S · 10% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
375
TOK/S · 10% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
361
TOK/S · 10% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
330
TOK/S · 10% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
330
TOK/S · 10% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
313
TOK/S · 11% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
313
TOK/S · 11% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
313
TOK/S · 11% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
303
TOK/S · 11% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
253
TOK/S · 12% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
250
TOK/S · 12% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
232
TOK/S · 12% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
216
TOK/S · 13% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
200
TOK/S · 13% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
174
TOK/S · 14% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
171
TOK/S · 15% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
156
TOK/S · 15% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
156
TOK/S · 15% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
156
TOK/S · 15% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
156
TOK/S · 15% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
144
TOK/S · 16% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
144
TOK/S · 16% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
144
TOK/S · 16% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
139
TOK/S · 16% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
132
TOK/S · 17% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
132
TOK/S · 17% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
125
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Radeon RX 570 Mobile.

WHAT THIS CARD IS WORTH

Radeon RX 570 Mobile 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 GB19 tok/sEST
Bamba 9B v29.78B · Q4_K_M7.2 GB19 tok/sEST
RecurrentGemma 9B9.63B · Q4_K_M6.8 GB19 tok/sEST
glm-4-9b9.4B · Q4_K_M6.6 GB19 tok/sEST
Yi 1.5 9B9B · Q4_K_M6.5 GB20 tok/sEST
Yi Coder 9B9B · Q4_K_M6.5 GB20 tok/sEST
Ministral 3 8B8.92B · Q4_K_M6.6 GB20 tok/sEST
Ministral 3 8B Reasoning8.92B · Q4_K_M6.6 GB20 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 RX 570 Mobile8 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 RX 570 Mobile — 8 GB VRAM.

RADEON RX 570 MOBILE SPEC
BRAND
AMD
VRAM
8 GB GDDR5
BANDWIDTH
211 GB/s
FP16 COMPUTE
4.9 TFLOPS
FP32 COMPUTE
4.9 TFLOPS
STREAM PROCESSORS
2,048
TDP
85 W
ARCHITECTURE
GCN 4.0
▸ AI CAPABILITY
201/ 449 models @ Q4

With 8 GB VRAM and 211 GB/s bandwidth, this GPU handles models up to 9.63B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~24 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RADEON RX 570 MOBILE
201 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
RecurrentGemma 9B9.63B6.4 GB1935.0
glm-4-9b9.4B6.2 GB2020.5
gemma-2-9b9.2B6.1 GB2030.2
Yi 1.5 9B9B6.0 GB2130.3
Yi Coder 9B9B6.0 GB2135.8
Ministral 3 8B8.92B5.9 GB2125.7
Ministral 3 8B Reasoning8.92B5.9 GB21
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB2144.2
InternLM3 8B Instruct8.8B5.9 GB2138.7
Gemma 1 7B8.54B5.7 GB2224.7
CodeGemma 7B8.54B5.7 GB2240.2
LFM2 8B A1B8.3B5.6 GB12524.3
Seed-Coder 8B Instruct8.25B5.5 GB2334.1
Seed-Coder 8B Reasoning8.25B5.5 GB2332.9
DeepSeek R1-0528 Qwen3 8B8.2B5.5 GB2336.3
Qwen3-8B8.2B5.5 GB2343.3
Granite 3.0 8B8.17B5.5 GB2336.4
Granite 3.1 8B8.17B5.5 GB2338.6
Command-R7B8.03B5.4 GB2335.3
Aya Expanse 8B8B5.4 GB2327.8