FitMyLLM
AMD· GCN 1.0

AMD Radeon R5 M320

Running LLMs on the Radeon R5 M320 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
4 GB
BUDGET
BANDWIDTH
16
GB/S
MODELS Q4
97/449
22%
7B Q4 SPEED
~2
SLOW
▸ MODEL COVERAGE @ Q422% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~5
TOK/S
7B
3.9GB NEEDED
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
4 GB
BANDWIDTH
16 GB/s
FP16 COMPUTE
0.5 TFLOPS
TDP
75W
MEMORY
DDR3
ARCHITECTURE
GCN 1.0
STREAM PROCESSORS
320
COMPUTE UNITS
5
PCIE
Gen 3 x8
13
FAST MODELS · >30 TOK/S
Real-time chat speed
39
USABLE · >10 TOK/S
Comfortable for all tasks
97
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Radeon R5 M320 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 97
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
158
TOK/S · 14% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
105
TOK/S · 14% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
102
TOK/S · 14% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
102
TOK/S · 14% VRAM
A
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
56
TOK/S · 16% VRAM
A
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
53
TOK/S · 16% VRAM
A
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
47
TOK/S · 17% VRAM
A
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
42
TOK/S · 17% VRAM
A
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
42
TOK/S · 17% VRAM
A
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
42
TOK/S · 17% VRAM
A
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
41
TOK/S · 18% VRAM
A
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
40
TOK/S · 18% VRAM
B
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
37
TOK/S · 18% VRAM
B
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
28
TOK/S · 20% VRAM
B
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
28
TOK/S · 20% VRAM
B
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
27
TOK/S · 20% VRAM
B
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
25
TOK/S · 21% VRAM
B
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
25
TOK/S · 21% VRAM
C
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
24
TOK/S · 21% VRAM
C
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
24
TOK/S · 21% VRAM
C
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
24
TOK/S · 21% VRAM
C
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
23
TOK/S · 22% VRAM
C
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
19
TOK/S · 24% VRAM
C
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
19
TOK/S · 24% VRAM
C
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
18
TOK/S · 25% VRAM
C
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
16
TOK/S · 26% VRAM
C
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
15
TOK/S · 27% VRAM
D
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
13
TOK/S · 29% VRAM
D
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
13
TOK/S · 29% VRAM
D
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
12
TOK/S · 31% VRAM
D
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
12
TOK/S · 31% VRAM
D
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
12
TOK/S · 31% VRAM
D
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
12
TOK/S · 31% VRAM
D
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
11
TOK/S · 33% VRAM
D
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
11
TOK/S · 32% VRAM
D
OPT 1.3B1.3B
OPT·2K CTX· CHAT
11
TOK/S · 32% VRAM
D
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
11
TOK/S · 32% VRAM
D
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
10
TOK/S · 34% VRAM
D
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
10
TOK/S · 34% VRAM
D
Falcon3-1B1.67B
FALCON·32K CTX· CHAT
9
TOK/S · 38% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Radeon R5 M320.

WHAT THIS CARD IS WORTH

Radeon R5 M320 holds 97 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is InternLM2 5B at Q4_K_M.

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
InternLM2 5B4.5B · Q4_K_M3.6 GB3 tok/sEST
Ministral 3 3B Reasoning4.25B · Q4_K_M3.3 GB3 tok/sEST
Qwen3 4B4B · Q4_K_M3.3 GB3 tok/sEST
Qwen3-4B Instruct 25074B · Q4_K_M3.3 GB3 tok/sEST
Qwen3-Embedding 4B4B · Q4_K_M3.3 GB3 tok/sEST
Nemotron 3 Nano 4B3.97B · Q4_K_M3.4 GB3 tok/sEST
Ministral 3 3B3.85B · Q5_K_M3.5 GB3 tok/sEST
phi-3-mini-4k 3.8B3.8B · Q4_K_M3.4 GB3 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3070 Ti8 GB$49912.6 tok/s per $100
RTX 3060 Ti GDDR6X8 GB$39915.8 tok/s per $100
RTX 3070 Ti 8 GB GA1028 GB$59910.5 tok/s per $100
Arc A7508 GB$19910.1 tok/s per $100
Radeon R5 M3204 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 R5 M320 — 4 GB VRAM.

RADEON R5 M320 SPEC
BRAND
AMD
VRAM
4 GB DDR3
BANDWIDTH
16 GB/s
FP16 COMPUTE
0.5 TFLOPS
FP32 COMPUTE
0.5 TFLOPS
STREAM PROCESSORS
320
TDP
75 W
ARCHITECTURE
GCN 1.0
▸ AI CAPABILITY
97/ 449 models @ Q4

With 4 GB VRAM and 16 GB/s bandwidth, this GPU handles models up to 4.25B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~2 tok/s.

§ 01TOP MODELS FOR RADEON R5 M320
97 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Ministral 3 3B Reasoning4.25B3.1 GB3
Qwen 1.5 4B4B2.9 GB412.6
Qwen3 4B4B2.9 GB440.7
Qwen3-4B Instruct 25074B2.9 GB437.2
Qwen3-Embedding 4B4B2.9 GB4
Nemotron 3 Nano 4B3.97B2.9 GB432.0
Ministral 3 3B3.85B2.8 GB421.4
Phi-3.5 Mini 3.8B3.82B2.8 GB446.6
phi-3-mini-4k 3.8B3.8B2.8 GB430.5
Phi-4-mini 3.8B3.8B2.8 GB449.0
Cogito 3B3.61B2.7 GB422.1
Falcon3-3B3.23B2.5 GB425.7
granite-4.0-h-micro 3.2B3.2B2.4 GB418.4
Llama-3.2-3B3.2B2.4 GB417.9
Falcon-H1 3B3.15B2.4 GB549.5
Qwen 2.5 3B3.1B2.4 GB537.2
SmolLM3-3B3.1B2.4 GB530.5
Ministral 3B3B2.3 GB529.6
StarCoder2 3B3B2.3 GB59.5
Granite 4.1 3B3B2.3 GB516.6