DeepSeek-V3 for local llms
DeepSeek-V3 is ranked #6 on LLMDex's local llms ranking out of 7 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #6 of 7
- Context
- 128K tokens
- Output / 1M
- $1.10 / 1M tokens
- Released
- Dec 2024
Why DeepSeek-V3 fits this task
Three things about DeepSeek-V3 that map directly onto what this task rewards: MIT license, clean commercial use. Beyond the task-specific fit, DeepSeek-V3 also brings frontier-level quality at open-weight prices and cheap to serve via moe architecture, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best local llms on 5 criteria , these are the axes the ranking uses, in priority order:
- Performance after 4-bit quantization
- Memory footprint at int4 / int8
- Inference speed on Apple Silicon and consumer GPUs
- Tooling support (Ollama, LM Studio, llama.cpp)
- License permits unlimited local use
How DeepSeek-V3 scores on each axis
Where DeepSeek-V3 costs you: no native vision support. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Frontier-level quality at open-weight prices
- MIT license, clean commercial use
- Cheap to serve via MoE architecture
- Strong code and math
Tracked weaknesses
- No native vision support
- Geopolitical concerns for some enterprise customers
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Phi-4 from Microsoft ranks one position higher and tends to win on the hardest cases. Microsoft's 14B model, exceptional quality-per-parameter via curated synthetic training data.
Run DeepSeek-V3 now
Skip setup. Deploy via a hosted provider in under a minute.
Other models for local llms
- Llama 4 8B for local llms
Meta's small Llama 4, built for on-device and edge inference.
Read guide - Llama 4 70B for local llms
Meta's mid-tier Llama 4, the practical workhorse for self-hosted deployments.
Read guide - Qwen2.5-72B for local llms
The previous-generation Qwen flagship, still widely deployed for stability.
Read guide - Qwen2.5-7B for local llms
Small Qwen, practical default for laptop and edge inference.
Read guide - Phi-4 for local llms
Microsoft's 14B model, exceptional quality-per-parameter via curated synthetic training data.
Read guide
DeepSeek-V3 for other use cases
Direct comparisons
Frequently asked
Is DeepSeek-V3 good for local llms?
DeepSeek-V3 is ranked #6 on LLMDex's local llms list. DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.How much does DeepSeek-V3 cost for local llms?
DeepSeek-V3 costs $0.27 / 1M tokens for input tokens and $1.10 / 1M tokens for output tokens. For local llms workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to DeepSeek-V3 for local llms?
The next ranked model on this task is Mistral Nemo. Compare both before committing.When should I NOT use DeepSeek-V3 for local llms?
Tracked weakness: No native vision support. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
Intelligence, distilled weekly.
One short email every Friday, new model launches, leaderboard moves, and pricing drops. Curated by hand. Free, no spam.