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⇱ RTX 4080 Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

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RTX 4080 LLM Performance

Local LLM Performance: 51.2 t/s average on 14B models at 16k context.

Gen (14B 4-bit) 51.2 t/s
PP (14B 4-bit) 2,295 t/s
Max Model 20B
VRAM
16 GB GDDR6X
Bandwidth 716 GB/s
Token Gen (14B @ 4k Ctx)

51.2T/s

Prompt Proc (14B @ 4k Ctx)

2,295T/s

Summary

In our testing, the NVIDIA GeForce RTX 4080 performed very well for local LLM inference. Its 16GB VRAM and 716 GB/s bandwidth allowed us to run models up to ~20B parameters in 4-bit quantization fully in VRAM, achieving about 51.2 tokens/s generation and 2,200+ tokens/s prompt processing on Qwen3 14B at 16K context. However, the value is questionable at full price. Compared with newer 16GB GPUs like the NVIDIA GeForce RTX 5060 Ti, the real-world performance difference in token generation and prompt processing is relatively small. It becomes a good option mainly if found at a good second-hand price.

Key Insights

Runs Qwen3 14B using GGUF (Q4_K) fully in VRAM with up to 32K context.
Delivers around 51.2 tokens/s generation on 14B 4-bit models at 16K context.
Supports models up to ~20B parameters fully offloaded in 4-bit quantization.
High prompt processing throughput reaching ~2,295 tokens/s on 14B models.
Large 716 GB/s memory bandwidth significantly improves prompt ingestion performance.

Current Price in US

$887

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 16GB GDDR6X
Capable of running 20B model
Bandwidth 716 GB/s
Architecture Ada Lovelace
Memory speed 22.4 Gbps
Memory bus 256 bit
TDP 320 W
Suggested PSU 750 W
Price/GB VRAM $55.44
Price/(t/s) with 14B @ 16k $17.32

Biggest LLMs You Can Run on This GPU

The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.

gpt-oss 20B (MXFP4) Max 128k
Token Generation 60.0 t/s @ 128k context
Prompt Processing 898.5 t/s @ 128k context
Qwen3 14B (Q4_K) Max 32k
Token Generation 40.6 t/s @ 32k context
Prompt Processing 1,395.7 t/s @ 32k context
Qwen3 8B (Q4_K) Max 64k
Token Generation 39.0 t/s @ 64k context
Prompt Processing 937.9 t/s @ 64k context

Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.

RTX 4080 local LLM Inference Performance vs Similar GPUs

Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.

Local LLM Benchmarks

Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.

Prompt Processing
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 6,177.9 3,809.8 1,968.4 937.9 — —
Qwen3 14B (Q4_K) 3,574.7 2,295.1 1,395.7 — — —
gpt-oss 20B (MXFP4) 6,218.5 4,329.2 2,831.1 1,559.0 898.5 —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 102.7 77.9 59.0 39.0 — —
Qwen3 14B (Q4_K) 62.0 51.2 40.6 — — —
gpt-oss 20B (MXFP4) 136.5 120.2 106.0 81.3 60.0 —

Frequently Asked Questions

Common questions about running LLMs on the RTX 4080.