Agens Volundr 32B v1 on a 16 GB GPU: what the Agens Engine build needs, how fast it is, and what quality it gives up

Blockway posted Agens Volundr 32B v1 to Hugging Face on October 17, 2026, and announced it on r/LocalLLaMA on October 18 with a disclosure that it is the team’s own model (Reddit post). The headline for local users is a new build that runs a 32B model with a 262K-token context inside a 16 GiB GPU memory budget. Every number below comes from Blockway’s own model cards and README. Nobody has published independent results yet.

What the model is

Volundr is a roughly 32B-parameter hybrid model under Apache-2.0. According to the main model card, 54 of its 72 layers use Kimi Delta Attention, a linear attention with a fixed-size state. 17 layers use Blockway’s sparse attention, and one is dense. So only 18 layers keep a KV cache, the same basic trick behind other hybrid-linear models like Ling 3.1 Flash. Blockway says v1 fixes the Preview’s repetition loops (36 of 50 SWE-bench tasks looped, now 0 of 50).

On Blockway’s own harness, v1 beats Qwen3.8-27B on LiveCodeBench v6 (67.9 vs 59.5) and AIME 2025 (80.0 vs 73.3). It trails Qwen3.8-27B on agentic coding (SWE-bench Verified 50-task subset: 54.0 vs 58.0), τ²-bench (76.7 vs 79.2) and GPQA Diamond (80.3 vs 85.4). Blockway ran the Qwen numbers itself, so these are not Qwen’s published figures.

Running it on 16 GB: the Agens Engine build

The Engine-16GB checkpoint stores each linear layer at 2, 3 or 4 bits, which works out to about 2.82 bits per weight. The KV cache is tiered into host RAM. It loads only in Agens Engine, an MIT-licensed server with an OpenAI-compatible API. Requirements from the README:

• A 16, 24 or 48 GB NVIDIA GPU with a CUDA 12.9-capable driver. Prebuilt kernels cover compute capability 8.9 and 12.0 only. That is GeForce RTX 40-series and RTX 50-series on NVIDIA’s compute capability list, so RTX 30-series cards (8.6) are not in the prebuilt set.
• 32 GB or more of host RAM for the full 262K context.
• Docker with the NVIDIA container toolkit.
• A 25 GB download, plus the separate DFlash2 drafter for speculative decoding.

The documented launch command is:

docker run --gpus device=0 --ipc=host -p 30000:30000 \
  -v /path/to/Agens-Volundr-32B-v1-Engine-16GB:/model:ro \
  -v /path/to/Agens-Volundr-32B-v1-DFlash2:/drafter:ro \
  ghcr.io/blockwayz/agens-engine:1.0.0 --profile 16gb --model /model --drafter /drafter

The 16gb profile serves one conversation. The 24gb and 48gb profiles allow batched requests.

Speed and quality, with the fine print

Blockway reports 90 to 139 tok/s at short context and 62 to 86 tok/s after a 219K-token document. Prefilling that document took 151 s, and peak GPU memory was 15.5 GiB. Read these with three caveats:

1. No real 16 GB card was used. The numbers come from a 48 GB card held to 16 GiB by the engine’s hard cap. Blockway warns that a GPU also driving a display has less free memory, so use a shorter context there. Its roadmap says compute capability 12.0 is “prebuilt today, not yet validated on hardware.”
2. The drafter only speeds up greedy requests. In Engine 1.0, speculative decoding runs only at temperature: 0. With sampling at 0.6, short chat drops to about 55 tok/s.
3. Quality drops at 2.8 bits. GPQA Diamond falls from 80.3 (bf16) to 73.7, and HumanEval from 78.7 to 73.2. Retrieval was validated only to 107K tokens, not the full 262K window. Engine 1.0 is also text only.

If you’d rather use llama.cpp

The GGUF repo has Q4_K_M (26.6 GB), Q8_0 (36.3 GB) and BF16 (64.5 GB). They need Blockway’s volundr branch of llama.cpp. Stock llama.cpp, and apps built on it, fail with unknown model architecture: 'volundr' until support is merged upstream. Even Q4_K_M is bigger than a 16 GB card, so on that route part of the model has to stay in system RAM. The card suggests keeping the four Engram lookup tables (4.3 GB) on the CPU with --override-tensor "engram_embd=CPU". If you’re sizing hardware, our RTX Spark memory-tier breakdown covers the bigger unified-memory options.