Intel · Alchemist · 8 GB VRAM tier
Best AI Models for Intel Arc A750 (8 GB)
Runs 7–9B models well; 14B doesn't fit in 8 GB.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 78.5 t/s | 78.5 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 42.0 t/s | 42.0 tok/s | Q4_K_M · 7.3 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 90.1 t/s | 90.1 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 118 t/s | 118 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 · 40.3 t/s | 40.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 90.1 t/s | 90.1 tok/s | Q4_K_M · 5.0 GB | |
| 3 | Qwen2.5 7B InstructAlibaba Q4_K_M · 5.8 GB · 51.3 t/s | 51.3 tok/s | Q4_K_M · 5.8 GB | |
| 4 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 42.0 t/s | 42.0 tok/s | overall | Q4_K_M · 7.3 GB |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 118 t/s | 118 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 78.5 t/s | 78.5 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 6.6 GB | |
| 3 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 48.1 t/s | 48.1 tok/s | Q4_K_M · 5.6 GB | |
| 4 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 118 t/s | 118 tok/s | Q4_K_M · 5.5 GB | |
| 5 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 135 t/s | 135 tok/s | Q4_K_M · 2.5 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 78.5 t/s | 78.5 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 118 t/s | 118 tok/s | Q4_K_M · 5.5 GB | |
| 3 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 48.1 t/s | 48.1 tok/s | Q4_K_M · 5.6 GB | |
| 4 | Q4_K_M · 6.5 GB · 41.2 t/s | 41.2 tok/s | Q4_K_M · 6.5 GB | |
| 5 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 6.6 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 78.5 t/s | 78.5 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 90.1 t/s | 90.1 tok/s | Q4_K_M · 5.0 GB | |
| 4 | Q4_K_M · 6.5 GB · 41.2 t/s | 41.2 tok/s | Q4_K_M · 6.5 GB | |
| 5 | MiniCPM V 4.7 1BOpenBMB Q4_K_M · 1.6 GB · 200 t/s | 200 tok/s | Q4_K_M · 1.6 GB |
The Arc A750 is a solid mid-range card in the 8 GB class: it runs 7–9B models well. It is power-hungry for its speed, and at its $289 MSRP it gave the best speed per dollar of the cards compared. Alchemist generation, released 2022.
Arc A750 strengths and weaknesses
Strengths
- Runs 7–9B models: Qwen3.5 9B at 40.3 tok/s in 6.6 GB
Weaknesses
- 14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 8 GB here
- Little room for context: Qwen3.5 9B uses 6.6 of 8 GB, 1.4 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 A750 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 A7508 GB | about $289 | 40.3 | 13.9 | 17.9 |
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 40608 GB | about $520 | 27.8 | 5.3 | 24.2 |
| 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 |
RTX 507012 GB | about $736 | 68.7 | 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 |
RTX 4070 Ti12 GB | about $722 | 51.5 | 7.1 | 18.1 |
Cost per token: the Arc A750 against 8–12 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
- $0.45
- Power
- $0.23
Renting costs as much as buying after ≈1,700 hours (≈1.2 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 A750, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (512 GB/s). Grey rows need more than 8 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB40.3tok/s
- 14BQwen3 14B · not at 4-bitneeds 11.9 GB
- 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 822 models on the Arc A750
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 |
|---|---|---|---|---|---|---|
Qwen3.5 9BVisionFunctions | Q4_K_M | 6.6 GB | 40.3 t/s | 262K | A71, Runs well | |
Gemma 4 E4B ITChatFunctions | Q4_K_M | 5.6 GB | 48.1 t/s | 131K | A79, Runs well | |
Ling 3.0 TinyChatFunctions | Q4_K_M | 5.5 GB | 118.3 t/s | 131K | S95, Runs great | |
MiMo V2.6 Distill Qwen 9BVisionCodeFunctions | Q4_K_M | 6.5 GB | 41.2 t/s | 262K | A74, Runs well | |
MiniCPM5 2BChatFunctions | Q4_K_M | 2.5 GB | 134.7 t/s | 131K | S95, Runs great | |
Qwen3.5 4BVisionFunctions | Q4_K_M | 3.4 GB | 78.5 t/s | 262K | S87, Runs great | |
Spark X2.6 4BChatFunctions | Q4_K_M | 4.3 GB | 86.5 t/s | 1049K | S89, Runs great | |
Ornith 1.5 9BChatFunctions | Q4_K_M | 6.6 GB | 40.3 t/s | 262K | A71, Runs well | |
NVIDIA Nemotron 3.5 Bolt E4B BF16ChatFunctions | Q4_K_M | 5.6 GB | 48.1 t/s | 66K | A79, Runs well | |
LFM3 1.7BChatFunctions | Q4_K_M | 2.1 GB | 177.8 t/s | 262K | S99, Runs great | |
MiniCPM V 4.7 1BVisionFunctions | Q4_K_M | 1.6 GB | 200.0 t/s | 262K | S100, Runs great | |
LFM2.5 8B A1BChatFunctions | Q4_K_M | 5.6 GB | 110.4 t/s | 128K | S93, Runs great | |
LFM3 Omni 2BVisionChatFunctions | Q4_K_M | 2.4 GB | 150.6 t/s | 128K | S97, Runs great | |
LFM3 VL 2BVisionChatFunctions | Q4_K_M | 2.3 GB | 165.2 t/s | 128K | S98, Runs great | |
Unlimited OCRVision | Q4_K_M | 3.3 GB | 177.1 t/s | 33K | S99, Runs great |
Specs
- Architecture
- Alchemist
- VRAM
- 8 GB GDDR6
- Memory bandwidth
- 512.0 GB/s
- FP16 (dense)
- 114.50 TFLOPS
- TDP
- 225 W
- Released
- 2022-10-12
- MSRP
- $289
Arc A750 FAQ
Is the Arc A750 still worth it in 2026?
At its MSRP of $289 you get 13.9 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 4 cards compared here; it is also the 6th fastest out of 12 cards with 8 GB. It runs 7–9B models well; 14B doesn't fit in 8 GB.
What is the largest model the Arc A750 can run?
Qwen3.5 9B (9.7B) is the largest at 4-bit or better: at Q4_K_M it uses 6.6 GB of 8 GB, and runs at about 40.3 tok/s.
Can the Arc A750 run 14B models?
No. Qwen3 14B needs 11.9 GB at Q4, more than the 8 GB on this card.
How many tokens per second does the Arc A750 give?
About 40.3 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 A750 need?
Plan a 450 W PSU (2× the 225 W TDP).
Is renting cheaper than buying an Arc A750?
An RTX 3090 (24 GB) rents from $0.17/hr. Renting costs as much as buying after ≈1,700 hours (≈1.2 years at 4 h a day).