Model benchmarks
lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF local LLM performance
As of October 2026, lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF runs at up to 18.3 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
16B
Peak speed
18.3 tok/s
Average speed
17.9 tok/s
Avg PP
206.0 tok/s
Min memory
n/a
Max context
65,535 tokens
Avg output / run
4,160 tokens
Avg runtime / run
4m 42s
Avg quality
40.9
Benchmark runs
4
GPUs tested
1
Quality by task
Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 4 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 35.6 | 40.9 | 45.5 |
| Agent Workflow | 35.7 | 50.2 | 59.6 |
| Code Generation | 17.1 | 32.5 | 47.8 |
| Role Play & Narrative | 33.3 | 50.1 | 60.5 |
| Research & Analysis | 25.7 | 30.8 | 33.9 |
Performance by hardware and tool
Every hardware/tool/quantization combination lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| Intel Arc B390 | llama.cpp | — | 18.3 tok/s | 17.9 tok/s | n/a | 65,535 tokens | 40.9 | 4 |
Benchmark runs
All 4 lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF good for coding?
- In our benchmarks, lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF scores 32.5/100 for coding. It runs at about 17.9 tok/s, so if you want more speed, IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF:1 is faster (~25.7 tok/s) and still scores well for coding (85.0/100).
- Is lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF good for agentic (tool-using) tasks?
- In our benchmarks, lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF scores 50.2/100 for agentic workflows. It runs at about 17.9 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF for local inference?
- Across 4 community benchmark runs, lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF reaches up to 18.3 tok/s and averages 17.9 tok/s, with the fastest results on Intel Arc B390.
- Which tools have been used to run lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.