ASRock AI BOX-A395 + Qwen3.8-27B: 256K context on Strix Halo 128 GB
The ASRock AI BOX-A395 (Ryzen AI Max+ 395, Radeon 8060S, 128 GB of shared LPDDR5X-8000) runs Qwen3.8-27B multimodal locally, and here are the measured throughputs, the KV cache sizing, the native 262k context windows extensible to 1M, the operating-system comparison and the pricing.
The context
The Strix Halo (Ryzen AI Max+ 395) is a special case in the local LLM landscape: an APU with an Radeon 8060S iGPU (RDNA 3.5, 40 CU) and up to 128 GB of unified LPDDR5X shared between CPU, GPU, and NPU. The same memory serves as VRAM for the iGPU via the GTT (Graphics Translation Table) mechanism.
ASRock Industrial has industrialized this chip in the AI BOX-A395, a compact edge AI enclosure, which we evaluated on Qwen3.8-27B, the multimodal vision-language model published by Alibaba/Qwen on August 14, 2026 under the Apache-2.0 licence.
AI BOX-A395 specifications
| Component | Detail |
|---|---|
| APU | AMD Ryzen AI Max+ 395 (Strix Halo), 16 cores / 32 threads, Zen 5 + Zen 5c, up to 5.1 GHz |
| iGPU | Radeon 8060S, RDNA 3.5, 40 CU, gfx1151 |
| NPU | XDNA 2, ~50 TOPS INT8 |
| Memory | 128 GB LPDDR5X-8000 unified CPU/GPU/NPU (128 GB version) |
| Storage | 1 × M.2 2280 NVMe PCIe |
| Network | 10 GbE (depending on configuration), WiFi 6E/7 optional |
| USB ports | 2 × USB4, 1 × USB-C 3.2 Gen2, 2 × USB-A 3.2 Gen2, 2 × USB-A 2.0 |
| Power supply | AC 100-240 V, 400 W (built-in) |
| Supported OS | Windows 11, Linux (Ubuntu 24.04 LTS, Fedora 41+) |
Sources: ASRock Industrial spec sheet, Electronics-Lab coverage.
Qwen3.8-27B: multimodal Apache-2.0
Qwen3.8-27B was published on August 14, 2026 by Alibaba/Qwen, under the Apache-2.0 license. It is a multimodal vision-language model (image + video + text), based on a hybrid Gated DeltaNet (linear attention) + Gated Attention architecture.
| Parameter | Value (config.json) |
|---|---|
| Total parameters | 27 B (dense) |
| Hidden dimension | 5120 |
| Layers | 64 |
| Attention heads (Q) | 24 |
| KV heads (GQA) | 4 |
| Attention head dim | 256 |
| Linear head dim (DeltaNet) | 128 (V:48 heads, QK:16 heads) |
| FFN intermediate | 17,408 |
| Vocab size | 248,320 |
| Native context | 262,144 tokens (extensible to 1,000,000 via MTP) |
| Layer layout | 16 × (3 × (DeltaNet → FFN) → 1 × (Attention → FFN)) |
| Multi-Token Prediction (MTP) | Yes, trained multi-step |
Source: huggingface.co/Qwen/Qwen3.8-27B : config.json verbatim.
ROCm / Vulkan prerequisites
On Strix Halo, two approaches coexist for llama.cpp:
- Vulkan (recommended in 2026): native gfx1151 support, no HSA_OVERRIDE_GFX_VERSION needed, generally higher performance than HIP/ROCm. Ollama-vulkan, LM Studio, and llama.cpp Vulkan share the same backend.
- ROCm / HIP: required for vLLM batch serving. ROCm 7.13 officially supports RDNA 3.5 APUs. Always use
HSA_OVERRIDE_GFX_VERSION=11.5.1unless ROCm 7.14+ detects gfx1151 natively.
On Fedora 41+ or Ubuntu 24.04 LTS with kernel 6.13 or newer, rocminfo | grep gfx1151 should return the device, while on Windows 11 vLLM is unavailable but Vulkan works out of the box through LM Studio or Ollama-vulkan.
Sources: ROCm 7.13 Expanding Hardware, llama.cpp Strix Halo build, NixOS Discourse.
Measured benchmarks
96k is not the machine's ceiling, it is where the published serving profile was measured. The KV cache table below shows the native 262,144-token context fits at 48% of memory (Q8 model 30 GB + Q8 KV cache 32 GB = 62 GB of 128). The throughputs below therefore apply at 96k; at 256K expect a drop proportional to the cache volume re-read, without memory becoming the limiting factor.
Two community sources publish measured benchmarks of Qwen3.8-27B on Strix Halo 128 GB:
| Source | Configuration | Throughput |
|---|---|---|
| kyuz0 (r/LocalLLaMA, Q8/Q4 30k context, llama.cpp) | Qwen3.8-27B Q8 / Q4_K_M | ~208.42 tokens/s (generation, 30k ctx) |
| KyaniteLabs (tuned serving profile, MTP n12 + ngram speculation) | Qwen3.8-27B Q8, llama.cpp Vulkan | 59.7 tok/s cold, 148-158 tok/s warm @ 96k ctx |
Q4 is faster than Q8 in tokens per second, but Q8 retains an edge on the quality benchmarks MMLU, GPQA and code. At 96k context, warmup takes one to two minutes while the KV cache loads, after which throughput stabilizes.
Sources: kyuz0 r/LocalLLaMA benchmark, KyaniteLabs/qwen38-27b-strix-halo.
KV cache and context windows
The KV cache of Qwen3.8-27B is dominated by the 4 KV heads (GQA, 256 head_dim). Per token, in FP16: 2 × 64 × 4 × 256 × 2 = 262,144 bytes (~256 KiB/token).
| Context | FP16 | Q8 | Q4 |
|---|---|---|---|
| 32,768 (32k) | ~8.0 GiB | ~4.0 GiB | ~2.0 GiB |
| 65,536 (64k) | ~16.0 GiB | ~8.0 GiB | ~4.0 GiB |
| 131,072 (128k) | ~32.0 GiB | ~16.0 GiB | ~8.0 GiB |
| 262,144 (262k native) | ~64.0 GiB | ~32.0 GiB | ~16.0 GiB |
With 128 GB shared, the Q8 model takes 30 GB and the Q8 KV cache at 262k takes 32, for 62 GB out of 128, or 48% utilization, which leaves plenty of headroom to run other tools in parallel or to dedicate more memory to the KV cache for longer windows.
Sources: config.json Qwen3.8-27B, kyuz0 amd-strix-halo-toolboxes.
Windows vs Linux
For Qwen3.8-27B multimodal, Vulkan is today the path of least friction, and the following matrix summarizes the available options:
| OS | Backend | vLLM | Recommended use |
|---|---|---|---|
| Windows 11 | Vulkan (LM Studio, Ollama-vulkan, llama.cpp) | ❌ | Interactive development, multimodal |
| Linux (Fedora 41+, Ubuntu 24.04 HWE) | Vulkan or ROCm | ✅ ROCm | Batch serving, multimodal agentic |
For an AI BOX-A395 workstation, Linux Fedora 41+ with kernel 6.14 or newer is the preferred target, with vLLM, llama.cpp Vulkan, ROCm-SMI for monitoring and BIOS access to iGPU allocation, while Windows 11 remains valid for interactive use through LM Studio, without vLLM.
Source: KyaniteLabs/qwen38-27b-strix-halo.
Price comparison
The AI BOX-A395 128 GB is listed at $3,999 USD ex-VAT at MITXPC (US integrator, August 2026 pricing), which is about €3,435 ex-VAT at the 28 August 2026 rate of €1 = $1.164, before the margin added by the European distribution channel (Avnet, Arrow, MITXPC EU). The NVIDIA DGX Spark now trades around $4,699 ex-VAT after the memory-driven increase, about €4,037 ex-VAT, which places the ASRock roughly 15% below it.
| Machine | Unified RAM | 2026 indicative price | Use case |
|---|---|---|---|
| ASRock AI BOX-A395 | 128 GB shared LPDDR5X | ~€3,435 ex-VAT ($3,999) | Compact edge AI, multimodal local |
| Nvidia DGX Spark | 128 GB unified | ~€3,800 ex-VAT | CUDA, native vLLM |
| Framework Desktop | 128 GB | ~€1,900 ex-VAT (frame only) | Modular DIY |
| HP Z2 Mini G1a (Ryzen AI Max PRO) | 128 GB | ~€2,500 – €3,500 ex-VAT | ECC workstation |
| Minisforum MS-S1 MAX | 128 GB | ~€1,500 – €1,900 ex-VAT | Mini-ITX alternative |
| Mac Studio M3 Ultra | 192-512 GB | €4,000 – €5,500 ex-VAT | Faster in pure MLX |
For Qwen3.8-27B multimodal, the AI BOX-A395 sits roughly 15% below the DGX Spark at current pricing, with inverted tradeoffs: the DGX Spark has CUDA and native vLLM, while the AI BOX-A395 has ROCm still being finalized and Vulkan available today. The Mac Studio M5 Ultra, from €6,599 incl. VAT or about €5,499 ex-VAT, costs appreciably more, but it reaches 512 GB of unified memory served at 1.2 TB/s, with weaker vision compatibility.
Sources: MITXPC ($3,999.95 ex-VAT), HP Z2 Mini G1a, LinuxGizmos coverage.
Limits of this article
The tokens/s at 262k context (Qwen3.8-27B native limit) and 1M tokens (MTP extension) remain to be measured on the AI BOX-A395 specifically. The community publishes regularly on r/StrixHalo and r/LocalLLaMA: as deeper benchmarks arrive (ETA Prime, Phoronix), we update this article.
The AI BOX-A395 is sold through industrial B2B channels, so the catalog price is not public and a quote is required, the market estimates above being provided for indicative purposes only.
Official sources
- ASRock Industrial : AI BOX-A395 (product sheet)
- Qwen/Qwen3.8-27B : Hugging Face (model card)
- unsloth/Qwen3.8-27B-GGUF (quants)
- AMD Ryzen AI Max+ 395
- AMD ROCm 7.13 : Expanding Hardware and Reach
- ROCm system-optimization RDNA 3.5
- llama.cpp : Strix Halo gfx1151 build
- KyaniteLabs/qwen38-27b-strix-halo : tuned serving profile
- Phoronix : ROCm 7.9 Strix Halo early tests
- r/LocalLLaMA : Qwen3.5/3.8 benchmarks Strix Halo
- kyuz0 amd-strix-halo-toolboxes dashboard
Frequently asked questions
Is the ASRock AI BOX-A395 the right choice for Qwen3.8-27B?
Qwen3.8-27B or Qwen3.5-27B-Instruct?
ROCm or Vulkan for llama.cpp?
Which quantization should I choose?
How much does the AI BOX-A395 cost?
Is the ASRock AI BOX-A395 the right choice to run Qwen3.8-27B locally?
Yes, under three conditions. First, Qwen3.8-27B is multimodal (vision + text), not a text-only LLM: Qwen3.5-27B-Instruct remains the choice for text-only workloads. Second, the 128 GB shared LPDDR5X-8000 lets you load the Q8 model (~30 GB) plus a comfortable KV cache for 96k context (~25-30 GB in Q8 KV). Third, the Vulkan backend of llama.cpp is today more performant and stable than ROCm/HIP on gfx1151 : start with Vulkan, switch to ROCm if a specific workload demands it.
Why Qwen3.8-27B over another 27B model?
Qwen3.8-27B was published by Alibaba/Qwen on August 14, 2026 under Apache-2.0. It is a multimodal model with a hybrid Gated DeltaNet (linear attention) + Gated Attention mechanism, native context of 262,144 tokens extensible to 1,000,000 via MTP (Multi-Token Prediction). For local VL, it combines image/video understanding and agentic reasoning that do not exist in text-only Qwen3.5-27B. For text-only, Qwen3.5-27B-Instruct is faster and lighter.
How many tokens per second can be expected on the AI BOX-A395?
Depends on quantization and context. Per kyuz0 (r/LocalLLaMA, Qwen3.5/Qwen3.8 30k context, llama.cpp), Qwen3.8-27B Q8 reaches about 208 tokens per second in generation. Per KyaniteLabs (tuned serving profile, MTP n12 + ngram speculation, 96k context), cold start is 59.7 tokens/s and warm reaches 148-158 tokens/s. At 262k context, expect a proportional throughput drop (typically divided by 3-4 compared to 30k).
Is the ROCm backend required or does Vulkan suffice?
On Strix Halo (gfx1151), Vulkan is today the recommended backend for llama.cpp: no HSA_OVERRIDE_GFX_VERSION override needed, native support, generally higher performance than HIP/ROCm. ROCm 7.13 officially supports RDNA 3.5 APUs, but for llama.cpp or ollama-vulkan, Vulkan remains the path of least friction. For vLLM batch serving, ROCm is mandatory and Linux is required (no Windows vLLM).
What are the prerequisites to install Qwen3.8-27B on the AI BOX-A395?
Three prerequisites. OS: Fedora 41+ or Ubuntu 24.04 LTS with kernel 6.13+ (amdgpu upstream stable for gfx1151), or Windows 11 + Vulkan if you do not need vLLM. Backend: llama.cpp compiled with -DGGML_VULKAN=1 (recommended), vLLM ROCm for batch serving. Model: download Unsloth GGUFs (Q4_K_M for VRAM savings, Q8_0 for quality) from huggingface.co/unsloth/Qwen3.8-27B-GGUF.
How much does the ASRock AI BOX-A395 cost?
The AI BOX-A395 128 GB is listed at $3,999 USD ex-VAT at MITXPC (August 2026), about €3,435 ex-VAT at the 28 August 2026 rate of €1 = $1.164, before European channel margin. The NVIDIA DGX Spark trades around $4,699 ex-VAT, about €4,037 ex-VAT, placing the ASRock roughly 15% below it. Distribution is through industrial B2B channels (Avnet, Arrow, MITXPC EU): no public MSRP, quote required.