Indras-Mirror / Xolotl

Xolotl 
 DeepSeek-V4.1-Flash - all 552 billion parameters - on one gaming PC, at 27 tok/s. 
Plus GLM-5.3-Flash, Qwen3.8-Flash-Next and Qwen3.8-27B: one engine, one OpenAI / Anthropic-compatible server. 
Built on Strata and heavily extended · Linux · NVIDIA · MIT 
> **A Strata fork.** Xolotl keeps Strata's engine, server and kernels at its
> core (merged at 0.1.44.1) and adds ~71,000 lines on top: the DeepSeek-V4.1 and V4-Flash engines, the GLM-5.3-Flash
> engine and the Qwen3.8-27B dense port, each with its own quantisation and expert-tiering work.
>
> **Starred this as Strata-DSV4.1?** You are in the right place. Strata-DSV4.1 grew into **Xolotl**: the same
> DeepSeek-V4.1 engine (faster now), plus GLM-5.3-Flash and the Qwen3.8 models, in one tree. Old links and clones
> redirect here; nothing DeepSeek-V4.1 was removed.
## What one PC can do
**One RTX 4090 (24 GB) · Ryzen 7 5700X · 90 GB DDR4 · one NVMe drive.** Every number is measured on that machine,
through the real server, while it solved real tasks (21 HumanEval problems, 10 tool-call tasks, 3 agentic bug-fix
loops), greedy, thinking on with a 32K-token thinking budget.
| Model | Size | Prefill | Decode | HumanEval | Tool calls | Agentic | Context |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| **DeepSeek-V4.1-Flash** | 552B MoE | **180-320 tok/s** | **26.8 tok/s** | 21/21 | 10/10 | 3/3 | 512K |
| **GLM-5.3-Flash** | 320B MoE | **290-375 tok/s** | **25.3 tok/s** | 19/21 | 10/10 | 3/3 | 512K |
| **Qwen3.8-Flash-Next** | 125B MoE | **3,000-3,430 tok/s** | **127.8 tok/s** | 21/21 | 10/10 | 3/3 | 200K |
| **Qwen3.8-27B** (dense) | 27B | **1,240-1,390 tok/s** | **98.5 tok/s** | 21/21 | 10/10 | 3/3 | 200K |
 Decode = generation speed during those tasks. Prefill = a fresh 6K-17K-token prompt through the server
(`tools/throughput.py`; Qwen3.8-Flash-Next: 3,430 / 3,005 tok/s at 6.5K / 16.7K, Qwen3.8-27B: 1,385 / 1,244 tok/s,
DeepSeek-V4.1: 178 / 268 tok/s at 6.1K / 16.5K - its prompt batch is sized from free VRAM, ~6-9K tokens per sweep
on a 24 GB card with the speculative-decode draft loaded).
DeepSeek-V4.1 with the speculative-decode draft off the card during long prompts (the default since 2026-10-17):
187 / 317 tok/s at 6.4K / 16.7K. GLM-5.3-Flash: soft prune + `--route-bias 0.25`
(`tools/glm/serve/strata-glm-maya-abl-512k-bias.json`), prefill 301 / 374 tok/s at 6.1K / 15.6K and 368-376 on fresh
30K / 44K prompts. How close the DeepSeek quant gets to DeepSeek's own API on the same tasks:
docs/xolotl/BENCHMARKS.md. 
**What that means in practice:**
- **A 552B frontier model as a local coding agent.** Point Claude Code, Codex, opencode or Open WebUI at it - tool
 calls, streaming, prefix reuse across agent turns, a thinking budget.
- **No server farm.** The weights (152 GiB for DeepSeek-V4.1) live across VRAM, pinned RAM and NVMe; the engine
 carries up the experts each token needs.
- **Fast where it counts.** The Qwen models run at 100-130 tok/s; an agent's small follow-up turn on D