FitMyLLM
▸ AMD· TERASCALE 3

AMD FirePro V7900 SDI

Running LLMs on the FirePro V7900 SDI — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
2 GB
BUDGET
BANDWIDTH
160
GB/S
MODELS Q4
47/449
10%
7B Q4 SPEED
~20
GOOD
▸ 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
160 GB/s
FP16 COMPUTE
1.9 TFLOPS
TDP
150W
MEMORY
GDDR5
ARCHITECTURE
TeraScale 3
STREAM PROCESSORS
1,280
PCIE
Gen 2 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 FirePro V7900 SDI (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· 47
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
1580
TOK/S · 27% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1053
TOK/S · 29% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1016
TOK/S · 29% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1016
TOK/S · 29% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
556
TOK/S · 32% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
527
TOK/S · 33% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
466
TOK/S · 34% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
425
TOK/S · 35% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
425
TOK/S · 35% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
425
TOK/S · 35% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
406
TOK/S · 35% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
395
TOK/S · 35% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
374
TOK/S · 36% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
284
TOK/S · 40% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
284
TOK/S · 40% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
274
TOK/S · 40% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
250
TOK/S · 42% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
250
TOK/S · 42% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
237
TOK/S · 43% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
237
TOK/S · 43% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
237
TOK/S · 43% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
229
TOK/S · 43% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
192
TOK/S · 47% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
190
TOK/S · 47% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
176
TOK/S · 49% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
163
TOK/S · 51% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
151
TOK/S · 53% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
132
TOK/S · 57% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
129
TOK/S · 58% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
119
TOK/S · 61% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
119
TOK/S · 61% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
119
TOK/S · 61% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
119
TOK/S · 61% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
109
TOK/S · 64% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
109
TOK/S · 64% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
109
TOK/S · 64% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
105
TOK/S · 66% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
100
TOK/S · 68% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
100
TOK/S · 68% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
95
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your FirePro V7900 SDI.

WHAT THIS CARD IS WORTH

FirePro V7900 SDI 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 GB48 tok/sEST
Falcon3-1B1.67B · Q5_K_M1.8 GB48 tok/sEST
LFM2-VL 1.6B1.6B · Q5_K_M1.7 GB49 tok/sEST
Falcon-H1 1.5B1.55B · Q6_K1.7 GB45 tok/sEST
Qwen2.5-Coder-1.5B1.5B · Q6_K1.6 GB46 tok/sEST
Qwen2 Math 1.5B1.5B · Q6_K1.6 GB46 tok/sEST
Qwen 2.5 1.5B1.5B · Q6_K1.6 GB46 tok/sEST
Stella en 1.5B v51.5B · Q6_K1.6 GB46 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
FirePro V7900 SDI2 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 FirePro V7900 SDI — 2 GB VRAM.

▸ FIREPRO V7900 SDI SPEC
BRAND
AMD
VRAM
2 GB GDDR5
BANDWIDTH
160 GB/s
FP16 COMPUTE
1.9 TFLOPS
FP32 COMPUTE
1.9 TFLOPS
STREAM PROCESSORS
1,280
TDP
150 W
ARCHITECTURE
TeraScale 3
▸ AI CAPABILITY
47/ 449 models @ Q4

With 2 GB VRAM and 160 GB/s bandwidth, this GPU handles models up to 1.61B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~18 tok/s.

§ 01TOP MODELS FOR FIREPRO V7900 SDI
47 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-2 XL 1.5B1.61B1.5 GB885.1
stablelm-2-1_6b1.6B1.5 GB899.5
Falcon-H1 1.5B1.55B1.4 GB9243.8
Qwen2.5-Coder-1.5B1.5B1.4 GB9519.6
Qwen2 Math 1.5B1.5B1.4 GB9519.6
Qwen 2.5 1.5B1.5B1.4 GB9530.2
Yi Coder 1.5B1.5B1.4 GB9514.6
Stella en 1.5B v51.5B1.4 GB95—
Phi-1 1.3B1.42B1.4 GB1007.2
Phi-1.5 1.3B1.42B1.4 GB1007.2
DeepSeek Coder 1.3B1.35B1.3 GB10516.8
EXAONE-4.0-1.2B1.3B1.3 GB10918.9
OPT 1.3B1.3B1.3 GB1095.3
MiniCPM-V 4.61.3B1.3 GB10921.5
LFM2.5-1.2B-Thinking1.2B1.2 GB11919.6
Llama-3.2-1B1.2B1.2 GB11910.1
LFM2 1.2B1.2B1.2 GB11915.6
Zamba2 1.2B1.2B1.2 GB11941.5
TinyLlama 1.1B1.1B1.2 GB12913.6
MiniCPM5 1B1.08B1.1 GB13226.5