Intel · Battlemage · 12 GB VRAM tier
Best AI Models for Intel Arc B580 (12 GB)
Runs 7–9B models well; 14B only just fits.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 69.9 t/s | 69.9 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 28.4 t/s | 28.4 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 37.4 t/s | 37.4 tok/s | Q4_K_M · 7.3 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 112 t/s | 112 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Q4_K_M · 7.9 GB · 43.0 t/s | 43.0 tok/s | Q4_K_M · 7.9 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | overall | Q4_K_M · 6.6 GB |
| 3 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 69.9 t/s | 69.9 tok/s | overall | Q4_K_M · 3.4 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 27.7 t/s | 27.7 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 112 t/s | 112 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 41.3 t/s | 41.3 tok/s | Q4_K_M · 7.6 GB | |
| 3 | Q4_K_M · 7.9 GB · 43.0 t/s | 43.0 tok/s | Q4_K_M · 7.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 80.3 t/s | 80.3 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 27.7 t/s | 27.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 69.9 t/s | 69.9 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Q4_K_M · 7.9 GB · 43.0 t/s | 43.0 tok/s | Q4_K_M · 7.9 GB | |
| 4 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 28.3 t/s | 28.3 tok/s | Q4_K_M · 11.0 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 27.7 t/s | 27.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 69.9 t/s | 69.9 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 112 t/s | 112 tok/s | Q4_K_M · 5.5 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 41.3 t/s | 41.3 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | Q4_K_M · 6.6 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 27.7 t/s | 27.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 35.8 t/s | 35.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 69.9 t/s | 69.9 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 28.4 t/s | 28.4 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 80.3 t/s | 80.3 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 36.7 t/s | 36.7 tok/s | Q4_K_M · 6.5 GB |
The Arc B580 is an entry card in the 12 GB class: it runs 7–9B models well. It is power-hungry for its speed, and at its $249 MSRP it gave the best speed per dollar of the cards compared. Battlemage generation, released 2024.
Arc B580 strengths and weaknesses
Strengths
- Runs 14B models: Qwen3 14B at 24.0 tok/s in 11.9 GB
Weaknesses
- 27–31B models don't fit: Qwen3.8 27B needs 17.9 GB at Q4 vs 12 GB here
- Little room for context: Qwen3 14B uses 11.9 of 12 GB, 0.1 GB left
- oneAPI, not CUDA: No FP8 or NVFP4 weights and no vLLM AWQ or GPTQ; use GGUF builds
Speed per dollar and per watt
On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better. The Arc B580 has no current price, so it is shown at its MSRP.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
Arc B58012 GB | about $249 | 35.8 | 14.4 | 18.8 |
RTX 50608 GBBest new value | about $460 | 45.8 | 10.0 | 31.6 |
RTX 507012 GB | about $840 | 68.7 | 8.2 | 27.5 |
RTX 5070 Ti16 GB | about $1,170 | 91.6 | 7.8 | 30.5 |
RX 907016 GB | about $800 | 60.4 | 7.6 | 27.5 |
RX 9070 XT16 GB | about $815 | 60.4 | 7.4 | 19.9 |
RTX 40608 GB | about $520 | 27.8 | 5.3 | 24.2 |
RX 9060 XT 16GB16 GB | about $590 | 30.2 | 5.1 | 18.9 |
| Card | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RTX 50608 GBBest used value | about $388 | 45.8 | 11.8 | 31.6 |
RTX 3060 12GB12 GB | about $329 | 36.8 | 11.2 | 21.6 |
RX 907016 GB | about $650 | 60.4 | 9.3 | 27.5 |
RTX 407012 GB | about $575 | 51.5 | 9.0 | 25.8 |
RTX 40608 GB | about $335 | 27.8 | 8.3 | 24.2 |
RTX 4060 Ti 8GB8 GB | about $362 | 29.4 | 8.1 | 18.4 |
RTX 4070 SUPER12 GB | about $675 | 51.5 | 7.6 | 23.4 |
RX 9060 XT 16GB16 GB | about $480 | 30.2 | 6.3 | 18.9 |
Cost per token: the Arc B580 against 8–16 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, MSRP
- $0.44
- Power
- $0.22
Renting costs as much as buying after ≈1,300 hours (≈0.9 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. Cards without a price for the chosen basis are left out. How we calculate cost per token
Cards people weigh against the Arc B580, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (456 GB/s). Grey rows need more than 12 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB35.8tok/s
- 24.0tok/s
- 27–31BQwen3.8 27B · not at 4-bitneeds 17.9 GB
- 35B MoEQwen3.6 35B A3B · not at 4-bitneeds 22.0 GB
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 956 models on the Arc B580
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Qwen2 VL 2B InstructVision | Q4_K_M | 2.1 GB | 135.7 t/s | 33K | S96, Runs great | |
| Q4_K_M | 6.7 GB | 47.8 t/s | 33K | A79, Runs well | ||
| Q4_K_M | 8.6 GB | 38.4 t/s | — | A75, Runs well | ||
| Q4_K_M | 1.2 GB | 330.4 t/s | 16K | S100, Runs great | ||
Qwen3 4B Thinking 2507ChatFunctions | Q4_K_M | 4.2 GB | 78.6 t/s | 262K | S87, Runs great | |
| Q4_K_M | 6.2 GB | 42.3 t/s | 8K | A76, Runs well | ||
| Q4_K_M | 11.8 GB | 24.0 t/s | 41K | C35, Usable | ||
| Q4_K_M | 6.2 GB | 42.3 t/s | 8K | A76, Runs well | ||
Opt 1.3BChat | FP16 | 2.9 GB | 79.7 t/s | 2K | S88, Runs great | |
| Q4_K_M | 5.7 GB | 46.4 t/s | 8K | A78, Runs well | ||
| Q4_K_M | 7.7 GB | 85.1 t/s | 262K | S88, Runs great | ||
NuExtractChat | Q4_K_M | 4.2 GB | 67.1 t/s | 4K | S85, Runs great | |
Qwen3 32BChatFunctions | IQ2_XXS | 12 GB | 23.6 t/s | 41K | C32, Usable | |
Mistral 7B v0.2Chat | Q4_K_M | 6.8 GB | 46.4 t/s | 33K | A78, Runs well | |
Granite 3.0 1B A400m InstructChatFunctions | Q4_K_M | 1.3 GB | 154.1 t/s | 4K | S97, Runs great |
Specs
- Architecture
- Battlemage
- VRAM
- 12 GB GDDR6
- Memory bandwidth
- 456.0 GB/s
- FP16 (dense)
- 116.50 TFLOPS
- TDP
- 190 W
- Released
- 2024-12-12
- MSRP
- $249
Arc B580 FAQ
Is the Arc B580 still worth it in 2026?
At its MSRP of $249 you get 14.4 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 9th fastest out of 9 cards with 12 GB. It runs 7–9B models well; 14B only just fits.
What is the largest model the Arc B580 can run?
GPT OSS 20B (20.9B) is the largest at 4-bit or better: at IQ4_XS it uses 11.8 GB of 12 GB, and runs at about 66.6 tok/s.
Can the Arc B580 run Qwen3.5 9B?
Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 12 GB, and it is the best model that runs well on this card.
Can the Arc B580 run 27–31B models?
No. Qwen3.8 27B needs 17.9 GB at Q4, more than the 12 GB on this card.
How many tokens per second does the Arc B580 give?
About 35.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 3.8 s on a 4K-token prompt.
What power supply does the Arc B580 need?
Plan a 400 W PSU (2× the 190 W TDP).
Is 12 GB enough for coding and agents?
Yes for coding: Llama 3.1 8B Instruct, the only coding-scored model that fits, runs at about 43 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an Arc B580?
An RTX 3090 (24 GB) rents from $0.19/hr. Renting costs as much as buying after ≈1,300 hours (≈0.9 years at 4 h a day).