DeepSeek V3 0324: Self-Hosting & Deployment Guide
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.
What is DeepSeek V3 0324?
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.
Developer: DeepSeek. Released 24 Mar 2025. Context window 163,840 tokens, up to 147,456 output tokens.
How to run DeepSeek V3 0324 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 0324 VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 1369.1 GB | Estimate | 684.5B params × 2 bytes |
| AWQ 4-bit | 351.9 GB | File size | QuixiAI/DeepSeek-V3-0324-AWQ |
| MLX 4-bit | 377.6 GB | File size | mlx-community/DeepSeek-V3-0324-4bit |
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 0324 on your hardware
| Hardware | BF16 (full precision) | AWQ 4-bit | MLX 4-bit |
|---|---|---|---|
| NVIDIA H200141 GB | — | 3× | 4× |
| NVIDIA H10080 GB | — | 6× | 6× |
| NVIDIA A100 80GB80 GB | — | 6× | 6× |
| NVIDIA L40S48 GB | — | — | — |
GPUs needed per variant.
| Hardware | MLX 4-bit |
|---|---|
| Mac, 512 GBM3 Ultra | — |
| 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 |
|---|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | — | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | — | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | — | — | — |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | — | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | — | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Serve it yourself
pip install -U vllm
vllm serve deepseek-ai/DeepSeek-V3-0324pip install 'sglang[all]'
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324pip install -U mlx-lm
mlx_lm.generate --model mlx-community/DeepSeek-V3-0324-4bit --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) |
|---|---|---|---|---|---|---|
| DeepInfra | $0.24 | $0.9 | $0.135 | 163,840 | fp4 | 89.6% |
| SiliconFlow | $0.25 | $1 | — | 163,840 | fp8 | 98.8% |
| GMICloud | $0.29 | $1.14 | $0.11 | 163,840 | fp8 | 95.0% |
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.
Browse available modelsWhy Build on LLM.API?
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High-Throughput Batch Jobs
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Crush your backlogs
5 Core Capabilities
Broad reasoning gains over V3
MMLU-Pro rose from 75.9 to 81.2, GPQA from 59.1 to 68.4 and AIME from 39.6 to 59.4 against the original V3.
Front-end web generation
The update specifically improved executable HTML, CSS and JavaScript output and the look of generated pages and game front-ends.
More accurate function calling
The release fixed function-calling inconsistencies present in earlier V3 builds.
Chinese writing quality
Medium and long-form Chinese writing was realigned toward the R1 style, with better translation and multi-turn rewriting.
Open weights under MIT
Released openly under MIT, unlike the original V3's custom licence, making commercial self-hosting straightforward.
6 Most Valuable Use Cases
- Generating usable front-end components and interactive pages
- Tool-calling backends that need dependable function-call formatting
- Chinese and bilingual long-form writing and translation
- General coding assistance at open-weight pricing
- Multi-turn rewriting and editing workflows
- Self-hosted commercial deployments under MIT
When to Use — When NOT to Use
Use it if...
- You mainly generate front-end code and want executable output
- You need MIT-licensed open weights for commercial self-hosting
- Your workload is general chat and coding rather than deep reasoning
- You want a cheap, well-understood non-reasoning baseline
Avoid if...
- You need a reasoning model: this is a non-thinking checkpoint
- You want the strongest DeepSeek coding results — V3.1 and later score far higher on agent benchmarks
- You need multimodal input
- Your prompts exceed 128,000 tokens
BENCHMARKS
DeepSeek V3 0324 benchmark scores
Intelligence index
Scale: 0-100 index points
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 0324
Summarised from publicly published developer write-ups and the model's own documentation. Opinions are the sources’, not LLM.API’s.
- The release landed with no marketing — a single tweet and a model card — and reviewers found a much larger upgrade than the name suggested.
- Community testing put it at or near the top of non-reasoning models of its day, with comparisons to Claude 3.5 Sonnet recurring across write-ups.
- Front-end generation is the most cited practical improvement: Tailwind components and interactive pages that run without fixing.
- On today's independent index it sits below the tracked median, which matches its position as an older non-reasoning checkpoint.
SOURCES
Frequently Asked Questions
What is DeepSeek V3 0324?
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.
Who makes DeepSeek V3 0324?
DeepSeek V3 0324 is developed by DeepSeek. It was released on 24 Mar 2025.
What is the context window of DeepSeek V3 0324?
163,840 tokens, with up to 147,456 output tokens per response.
How much does DeepSeek V3 0324 cost?
The reference list price is $0.25 per 1M input tokens and $1 per 1M output tokens. The cheapest host right now is DeepInfra at $0.24 / $0.9 per 1M tokens.
Which providers host DeepSeek V3 0324?
DeepInfra, SiliconFlow, GMICloud.
What modalities does DeepSeek V3 0324 support?
Text input and text output. It supports tool calling, structured outputs, JSON mode.
Can I use DeepSeek V3 0324 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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