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⇱ RTX 5080 Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

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RTX 5080 LLM Performance

Local LLM Performance: 64.0 t/s average on 14B models at 16k context.

Gen (14B 4-bit) 64.0 t/s
PP (14B 4-bit) 2,542 t/s
Max Model 20B
VRAM
16 GB GDDR7
Bandwidth 960 GB/s
Token Gen (14B @ 4k Ctx)

64.0T/s

Prompt Proc (14B @ 4k Ctx)

2,542T/s

Summary

In our evaluation, the RTX 5080 performs efficiently but presents a poor value proposition for local LLM usage due to its price-to-memory ratio. While the move to GDDR7 memory delivers high bandwidth (960 GB/s) and fast token generation, the 16GB VRAM limit restricts users to models under 28B parameters. At current price point, it is difficult to recommend over the previous generation's RTX 4080, which offers similar model capacity and adequate speeds for this class of hardware at a lower cost. While the 5080 excels at specific tasks like MXFP4 inference, for general local LLM workloads, the 16GB buffer is a significant constraint relative to the price.

Key Insights

High-speed generation averaging 64 t/s on 14B models due to GDDR7 bandwidth.
Poor price-to-performance ratio for local LLMs; the RTX 4080 is a better value for 16GB cards.
VRAM capacity of 16GB limits maximum model size to approximately 28B parameters.
Excellent prompt processing speed of 2,542 t/s on 14B models.
Supports MXFP4 and NVFP4 quantization, allowing for faster inference on supported models like gpt-oss 20B.

Current Price in US

$1,400

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 16GB GDDR7
Capable of running 20B model
Bandwidth 960 GB/s
Architecture Blackwell
Memory speed 30 Gbps
Memory bus 256 bit
TDP 360 W
Suggested PSU 850 W
Price/GB VRAM $87.50
Price/(t/s) with 14B @ 16k $21.86

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 105.6 t/s @ 128k context
Prompt Processing 1,726.6 t/s @ 128k context
Qwen3 14B (Q4_K) Max 32k
Token Generation 51.9 t/s @ 32k context
Prompt Processing 1,326.1 t/s @ 32k context
Qwen3 8B (Q4_K) Max 64k
Token Generation 49.0 t/s @ 64k context
Prompt Processing 1,124.6 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 5080 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) 6,410.1 4,024.4 1,943.3 1,124.6 — —
Qwen3 14B (Q4_K) 3,820.5 2,542.0 1,326.1 — — —
gpt-oss 20B (MXFP4)
llama.cpp build: 8233
 9,146.1 7,560.5 5,906.9 3,041.1 1,726.6 —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 129.1 94.1 72.5 49.0 — —
Qwen3 14B (Q4_K) 80.6 64.0 51.9 — — —
gpt-oss 20B (MXFP4) 172.4 161.3 149.3 130.9 105.6 —

Frequently Asked Questions

Common questions about running LLMs on the RTX 5080.