FitMyLLM
▸ AMD· GCN 5.0

AMD Radeon Pro Vega 16

Running LLMs on the Radeon Pro Vega 16 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
4 GB
BUDGET
BANDWIDTH
307
GB/S
MODELS Q4
97/449
22%
7B Q4 SPEED
~39
FAST
▸ 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
~91
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
307 GB/s
FP16 COMPUTE
4.9 TFLOPS
TDP
75W
MEMORY
HBM2
ARCHITECTURE
GCN 5.0
STREAM PROCESSORS
1,024
COMPUTE UNITS
16
PCIE
Gen 3 x16
97
FAST MODELS · >30 TOK/S
Real-time chat speed
97
USABLE · >10 TOK/S
Comfortable for all tasks
97
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Radeon Pro Vega 16 (or anything bigger) without committing. Pay by the second, cancel anytime.

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

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▸ COMPATIBLE MODELS· 97
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
3032
TOK/S · 14% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
2021
TOK/S · 14% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1949
TOK/S · 14% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1949
TOK/S · 14% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1066
TOK/S · 16% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1011
TOK/S · 16% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
895
TOK/S · 17% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
815
TOK/S · 17% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
815
TOK/S · 17% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
815
TOK/S · 17% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
780
TOK/S · 18% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
758
TOK/S · 18% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
718
TOK/S · 18% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
546
TOK/S · 20% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
546
TOK/S · 20% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
525
TOK/S · 20% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
480
TOK/S · 21% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
480
TOK/S · 21% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
455
TOK/S · 21% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
455
TOK/S · 21% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
455
TOK/S · 21% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
440
TOK/S · 22% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
369
TOK/S · 24% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
364
TOK/S · 24% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
337
TOK/S · 25% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
314
TOK/S · 26% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
290
TOK/S · 27% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
253
TOK/S · 29% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
248
TOK/S · 29% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
227
TOK/S · 31% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
227
TOK/S · 31% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
227
TOK/S · 31% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
227
TOK/S · 31% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
210
TOK/S · 32% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
210
TOK/S · 32% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
210
TOK/S · 32% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
202
TOK/S · 33% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
192
TOK/S · 34% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
192
TOK/S · 34% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
182
TOK/S · 35% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Radeon Pro Vega 16.

WHAT THIS CARD IS WORTH

Radeon Pro Vega 16 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 GB50 tok/sEST
Ministral 3 3B Reasoning4.25B · Q4_K_M3.3 GB52 tok/sEST
Qwen3 4B4B · Q4_K_M3.3 GB54 tok/sEST
Qwen3-4B Instruct 25074B · Q4_K_M3.3 GB54 tok/sEST
Qwen3-Embedding 4B4B · Q4_K_M3.3 GB54 tok/sEST
Nemotron 3 Nano 4B3.97B · Q4_K_M3.4 GB55 tok/sEST
Ministral 3 3B3.85B · Q5_K_M3.5 GB50 tok/sEST
phi-3-mini-4k 3.8B3.8B · Q4_K_M3.4 GB56 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 Pro Vega 164 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 Vega 16 — 4 GB VRAM.

▸ RADEON PRO VEGA 16 SPEC
BRAND
AMD
VRAM
4 GB HBM2
BANDWIDTH
307 GB/s
FP16 COMPUTE
4.9 TFLOPS
FP32 COMPUTE
2.4 TFLOPS
STREAM PROCESSORS
1,024
TDP
75 W
ARCHITECTURE
GCN 5.0
▸ AI CAPABILITY
97/ 449 models @ Q4

With 4 GB VRAM and 307 GB/s bandwidth, this GPU handles models up to 4.25B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~35 tok/s.

§ 01TOP MODELS FOR RADEON PRO VEGA 16
97 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Ministral 3 3B Reasoning4.25B3.1 GB64—
Qwen 1.5 4B4B2.9 GB6812.6
Qwen3 4B4B2.9 GB6840.7
Qwen3-4B Instruct 25074B2.9 GB6837.2
Qwen3-Embedding 4B4B2.9 GB68—
Nemotron 3 Nano 4B3.97B2.9 GB6932.0
Ministral 3 3B3.85B2.8 GB7121.4
Phi-3.5 Mini 3.8B3.82B2.8 GB7146.6
phi-3-mini-4k 3.8B3.8B2.8 GB7230.5
Phi-4-mini 3.8B3.8B2.8 GB7249.0
Cogito 3B3.61B2.7 GB7622.1
Falcon3-3B3.23B2.5 GB8425.7
granite-4.0-h-micro 3.2B3.2B2.4 GB8518.4
Llama-3.2-3B3.2B2.4 GB8517.9
Falcon-H1 3B3.15B2.4 GB8749.5
Qwen 2.5 3B3.1B2.4 GB8837.2
SmolLM3-3B3.1B2.4 GB8830.5
Ministral 3B3B2.3 GB9129.6
StarCoder2 3B3B2.3 GB919.5
Granite 4.1 3B3B2.3 GB9116.6