Underestimated budget solution: radeon 780m iGPU

There are so many posts where people complaining about high prices and asking for solution <= 1000 EUR.

So, there is one solution to consider: PC/mini PC/laptop on Ryzen 7 260/Ryzen 9 8945HX/etc CPU with 780m iGPU and 64 Gb of DDR5 RAM.

Barebone mini PC costs around 300-400, used 2x 32Gb DDR5 SO-DIMM around 500, used SSD 50-100 in my area.

Here are my numbers on Ryzen 7 260, Ubuntu 26 with kernel params amdgpu.gttsize=49152 amd_iommu=off ttm.pages_limit=16777216 (48Gb of "VRAM") and llama.cpp with Vulkan.

All LLMs are Unsloth Q8 quants.

Qwen 3.6 35B-A3B

| model | size | params | backend | ngl | type_k | type_v | fa | dev | test | t/s | | ----------------------- | ---------: | ------: | ------- | --: | -----: | -----: | --: | ------- | -------: | ------------: | | qwen35moe 35B.A3B Q8_0 | 35.19 GiB | 35.51 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | pp8192 | 287.33 ± 2.06 | | qwen35moe 35B.A3B Q8_0 | 35.19 GiB | 35.51 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | pp16384 | 263.51 ± 1.06 | | qwen35moe 35B.A3B Q8_0 | 35.19 GiB | 35.51 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | tg128 | 21.06 ± 0.01 | | qwen35moe 35B.A3B Q8_0 | 35.19 GiB | 35.51 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | tg256 | 20.85 ± 0.20 | 

Gemma 4 31B:

| model | size | params | backend | ngl | type_k | type_v | fa | dev | test | t/s | | ---------------- | ---------: | -------: | ------- | --: | -----: | -----: | --: | -------- | -------: | ------------: | | gemma4 31B Q8_0 | 30.38 GiB | 30.70 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | pp8192 | 51.59 ± 0.07 | | gemma4 31B Q8_0 | 30.38 GiB | 30.70 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | pp16384 | 46.59 ± 0.01 | | gemma4 31B Q8_0 | 30.38 GiB | 30.70 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | tg128 | 2.46 ± 0.00 | | gemma4 31B Q8_0 | 30.38 GiB | 30.70 B | Vulkan | 99 | q8_0 | q8_0 | 1 | Vulkan0 | tg256 | 2.30 ± 0.22 | 

For real tasks I'm using MTP, so tg numbers are higher, like for Gemma4 31B:

16.27.894.079 I slot print_timing: id 0 | task 0 | prompt eval time = 481467.90 ms / 20470 tokens ( 23.52 ms per token, 42.52 tokens per second) 16.27.894.088 I slot print_timing: id 0 | task 0 | eval time = 449250.04 ms / 2587 tokens ( 173.66 ms per token, 5.76 tokens per second) 16.27.894.089 I slot print_timing: id 0 | task 0 | total time = 930717.94 ms / 23057 tokens 16.27.894.099 I slot print_timing: id 0 | task 0 | graphs reused = 658 16.27.894.109 I slot print_timing: id 0 | task 0 | draft acceptance = 0.95566 ( 1918 accepted / 2007 generated), mean len = 3.87 

Bonus

If you have a laptop with additional small GPU like RTX 5060 8Gb, it can give some boost.

For dense models it is mostly useless, I only could get Gemma4 31B running in `draft-simple` mode with drafter Gemma4 E2B on GPU, which gave like 5-6 => 6-7 tg boost.

But for MoE you can use partial experts offloading which gives a greater boost for tg, but for a slower pp.

Qwen 3.6 35B-A3B Q8 MTP (--spec-type draft-mtp --spec-draft-n-max 3 --n-cpu-moe 37):

14.22.441.606 I slot print_timing: id 0 | task 233 | prompt eval time = 28373.08 ms / 2677 tokens ( 10.60 ms per token, 94.35 tokens per second) 14.22.441.611 I slot print_timing: id 0 | task 233 | eval time = 9697.76 ms / 338 tokens ( 28.69 ms per token, 34.85 tokens per second) 14.22.441.611 I slot print_timing: id 0 | task 233 | total time = 38070.84 ms / 3015 tokens 14.22.441.612 I slot print_timing: id 0 | task 233 | graphs reused = 289 14.22.441.615 I slot print_timing: id 0 | task 233 | draft acceptance = 0.85614 ( 244 accepted / 285 generated), mean len = 3.57 

I know number are not whopping, and you can't run DeepSeek on it. But is there a better solution for that money?