FitMyLLM
AMD· TERASCALE

AMD FireGL V8650

Running LLMs on the FireGL V8650 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
111
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~14
USABLE
▸ MODEL COVERAGE @ Q410% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
1.7GB NEEDED
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
2 GB
BANDWIDTH
111 GB/s
FP16 COMPUTE
0.4 TFLOPS
TDP
75W
MEMORY
GDDR4
ARCHITECTURE
TeraScale
STREAM PROCESSORS
320
COMPUTE UNITS
5
PCIE
Gen 1 x16
47
FAST MODELS · >30 TOK/S
Real-time chat speed
47
USABLE · >10 TOK/S
Comfortable for all tasks
47
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test FireGL V8650 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1096
TOK/S · 27% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
731
TOK/S · 29% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
705
TOK/S · 29% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
705
TOK/S · 29% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
385
TOK/S · 32% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
365
TOK/S · 33% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
323
TOK/S · 34% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
295
TOK/S · 35% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
295
TOK/S · 35% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
295
TOK/S · 35% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
282
TOK/S · 35% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
274
TOK/S · 35% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
260
TOK/S · 36% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
197
TOK/S · 40% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
197
TOK/S · 40% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
190
TOK/S · 40% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
174
TOK/S · 42% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
174
TOK/S · 42% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
164
TOK/S · 43% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
164
TOK/S · 43% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
164
TOK/S · 43% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
159
TOK/S · 43% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
133
TOK/S · 47% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
132
TOK/S · 47% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
122
TOK/S · 49% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
113
TOK/S · 51% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
105
TOK/S · 53% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
91
TOK/S · 57% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
90
TOK/S · 58% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
82
TOK/S · 61% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
82
TOK/S · 61% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
82
TOK/S · 61% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
82
TOK/S · 61% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
76
TOK/S · 64% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
76
TOK/S · 64% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
76
TOK/S · 64% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
73
TOK/S · 66% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
69
TOK/S · 68% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
69
TOK/S · 68% VRAM
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
66
TOK/S · 70% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your FireGL V8650.

WHAT THIS CARD IS WORTH

FireGL V8650 holds 47 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Moondream2 1.9B 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
Moondream2 1.9B1.9B · Q4_K_M1.7 GB34 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB33 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB34 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB32 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB32 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB32 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB32 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB32 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
FireGL V86502 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 FireGL V8650 — 2 GB VRAM.

FIREGL V8650 SPEC
BRAND
AMD
VRAM
2 GB GDDR4
BANDWIDTH
111 GB/s
FP16 COMPUTE
0.4 TFLOPS
FP32 COMPUTE
0.4 TFLOPS
STREAM PROCESSORS
320
TDP
75 W
ARCHITECTURE
TeraScale
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 111 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~13 tok/s.

§ 01TOP MODELS FOR FIREGL V8650
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB615.1
stablelm-2-1_6b1.6B1.5 GB629.5
Falcon-H1 1.5B1.55B1.4 GB6443.8
Qwen2.5-Coder-1.5B1.5B1.4 GB6619.6
Qwen2 Math 1.5B1.5B1.4 GB6619.6
Qwen 2.5 1.5B1.5B1.4 GB6630.2
Yi Coder 1.5B1.5B1.4 GB6614.6
Stella en 1.5B v51.5B1.4 GB66
Phi-1 1.3B1.42B1.4 GB697.2
Phi-1.5 1.3B1.42B1.4 GB697.2
DeepSeek Coder 1.3B1.35B1.3 GB7316.8
EXAONE-4.0-1.2B1.3B1.3 GB7618.9
OPT 1.3B1.3B1.3 GB765.3
MiniCPM-V 4.61.3B1.3 GB7621.5
LFM2.5-1.2B-Thinking1.2B1.2 GB8219.6
Llama-3.2-1B1.2B1.2 GB8210.1
LFM2 1.2B1.2B1.2 GB8215.6
Zamba2 1.2B1.2B1.2 GB8241.5
TinyLlama 1.1B1.1B1.2 GB9013.6
MiniCPM5 1B1.08B1.1 GB9126.5