DeepSeek V3.1 Terminus: Self-Hosting & Deployment Guide
DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's…
What is DeepSeek V3.1 Terminus?
DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's…
Developer: DeepSeek. Released 22 Sep 2025. Context window 163,840 tokens, up to 32,768 output tokens.
How to run DeepSeek V3.1 Terminus yourself
Open weights (mit), 684.5B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
DeepSeek V3.1 Terminus VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 1369.1 GB | Estimate | 684.5B params × 2 bytes |
| AWQ 4-bit | 348.9 GB | File size | adlik/DeepSeek-V3.1-Terminus-AWQ-W4AFP8 |
| MLX 4-bit | 377.6 GB | File size | mlx-community/DeepSeek-V3.1-Terminus-4bit |
| GGUF Q8_0 | 713.3 GB | File size | unsloth/DeepSeek-V3.1-Terminus-GGUF |
| GGUF Q4_K_M | 405.4 GB | File size | unsloth/DeepSeek-V3.1-Terminus-GGUF |
| GGUF Q2_K | 245.7 GB | File size | unsloth/DeepSeek-V3.1-Terminus-GGUF |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy DeepSeek V3.1 Terminus on your hardware
| Hardware | BF16 (full precision) | AWQ 4-bit | MLX 4-bit | GGUF Q8_0 | GGUF Q4_K_M | GGUF Q2_K |
|---|---|---|---|---|---|---|
| NVIDIA H200141 GB | — | 3× | 4× | 6× | 4× | 3× |
| NVIDIA H10080 GB | — | 6× | 6× | — | 6× | 4× |
| NVIDIA A100 80GB80 GB | — | 6× | 6× | — | 6× | 4× |
| NVIDIA L40S48 GB | — | — | — | — | — | 6× |
GPUs needed per variant.
| Hardware | GGUF Q8_0 | GGUF Q4_K_M | GGUF Q2_K |
|---|---|---|---|
| RTX 509032 GB | — | — | — |
| RTX 409024 GB | — | — | — |
| RTX 309024 GB | — | — | — |
GGUF via llama.cpp; more cards or CPU offload for larger files.
| Hardware | GGUF Q8_0 | GGUF Q4_K_M | GGUF Q2_K | MLX 4-bit |
|---|---|---|---|---|
| Mac, 512 GBM3 Ultra | — | — | Fits | — |
| Mac, 192 GBM2 Ultra | — | — | — | — |
| Mac, 128 GBM4 Max / M3 Max | — | — | — | — |
| Mac, 64 GBM4 Pro / Max | — | — | — | — |
Assumes ~75% of unified memory is usable by the GPU.
| Hardware | BF16 (full precision) | AWQ 4-bit | MLX 4-bit | GGUF Q8_0 | GGUF Q4_K_M | GGUF Q2_K |
|---|---|---|---|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | — | Fits | Fits | — | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | — | Fits | Fits | — | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | — | — | — | — | — | — |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | — | Fits | Fits | — | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | — | Fits | Fits | — | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Serve it yourself
pip install -U vllm
vllm serve deepseek-ai/DeepSeek-V3.1-Terminuspip install 'sglang[all]'
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3.1-Terminusllama-server -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:Q4_K_M \
--ctx-size 32768 --n-gpu-layers 99pip install -U mlx-lm
mlx_lm.generate --model mlx-community/DeepSeek-V3.1-Terminus-4bit --prompt "Hello"Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Providers
Companies that host this model today, with their public list prices. LLM API does not route this model yet.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices from OpenRouter's public catalogue.
| Provider | Input /M | Output /M | Cache read /M | Context | Precision | Uptime (24h) |
|---|---|---|---|---|---|---|
| SiliconFlow | $0.27 | $1 | — | 163,840 | fp8 | 98.7% |
| Novita | $0.27 | $1 | $0.135 | 131,072 | fp8 | 100.0% |
| AtlasCloud | $0.3 | $0.95 | $0.13 | 131,072 | fp8 | 98.4% |
| StreamLake | $0.3426 | $1.0284 | — | 128,000 | — | 99.9% |
Source: OpenRouter public catalogue. Last updated 23 Sep 2026.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
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Crush your backlogs
5 Core Capabilities
Refreshed V3.1 checkpoint
A September update to V3.1 that keeps the same architecture and 128K context while re-tuning output consistency.
Cleaner language mixing
The update targets stray Chinese-English mixing and occasional odd characters reported in V3.1 output.
Stronger agent behaviour
Tuning concentrated on the code agent and search agent paths, the workloads V3.1 was already used for.
Hybrid inference retained
Thinking and non-thinking modes still share one set of weights, chosen by prompt template.
Open weights under MIT
Published openly, so multiple providers host it and the same checkpoint can be self-hosted.
6 Most Valuable Use Cases
- Drop-in replacement for V3.1 in existing agent pipelines
- Coding agents where stray language mixing in output caused rework
- Search-and-summarise agents chaining retrieval with generation
- Long-document analysis inside a 128K context window
- Bilingual Chinese and English content workflows
- Cost-controlled reasoning at open-weight pricing
When to Use — When NOT to Use
Use it if...
- You already run V3.1 and want its output consistency fixes
- Your agents mix tool calls with long-context reading
- You want V3.1 behaviour at a lower published reference price
- You need open weights you can host yourself
Avoid if...
- You need multimodal input — this model is text only
- Your prompts exceed 128,000 tokens
- You want the newest DeepSeek generation rather than a V3.1 refresh
- You depend on benchmarks that were only published for the original V3.1
BENCHMARKS
DeepSeek V3.1 Terminus benchmark scores
Intelligence index
Scale: 0-100 index points
Reference price per 1M tokens
Bars compare published reference list prices, not LLM.API pricing.
Scores as published by Artificial Analysis and the official model card (source). Reference prices are provider list prices, not LLM.API pricing. Figures with no published value are omitted.
COMMUNITY
What developers say about DeepSeek V3.1 Terminus
Summarised from publicly published developer write-ups and the model's own documentation. Opinions are the sources’, not LLM.API’s.
- Hands-on write-ups frame Terminus as a consistency release rather than a capability jump over V3.1.
- Testers single out fewer language-mixing glitches as the most visible day-to-day difference.
- Independent scoring puts it level with V3.1 on the intelligence index, at a lower published reference price.
SOURCES
Frequently Asked Questions
What is DeepSeek V3.1 Terminus?
DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's…
Who makes DeepSeek V3.1 Terminus?
DeepSeek V3.1 Terminus is developed by DeepSeek. It was released on 22 Sep 2025.
What is the context window of DeepSeek V3.1 Terminus?
163,840 tokens, with up to 32,768 output tokens per response.
How much does DeepSeek V3.1 Terminus cost?
The reference list price is $0.27 per 1M input tokens and $1 per 1M output tokens. The cheapest host right now is SiliconFlow at $0.27 / $1 per 1M tokens.
Which providers host DeepSeek V3.1 Terminus?
SiliconFlow, Novita, AtlasCloud, StreamLake.
What modalities does DeepSeek V3.1 Terminus support?
Text input and text output. It supports tool calling, structured outputs, JSON mode, reasoning.
Can I use DeepSeek V3.1 Terminus through LLM API?
Not yet. LLM API does not route this model at the moment. Browse the models page for close alternatives you can call today with one API key.
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