Qwen 2.5 7B on RTX 5080: Performance Benchmark & Cost, Category: Benchmarks, Slug: qwen-2.5-7b-on-rtx-5080-benchmark, Excerpt: Qwen 2.5 7B benchmarked on RTX 5080: 66.5 tok/s at FP16, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

66.5 tokens per second puts Qwen 2.5 7B into the range where multilingual output streams faster than most people can read it. On the RTX 5080, the Blackwell architecture’s raw compute advantage turns what was already a fast model into something that feels instantaneous — type a prompt in Japanese, get a response before your eyes finish scanning to the output box. For real-time applications like live translation overlays, interactive language tutoring, or multilingual customer chat where perceived latency drives user satisfaction, this speed tier changes what is architecturally possible on a single GigaGPU dedicated server.

Qwen 2.5 7B Performance on RTX 5080

Benchmark conditions: single-stream generation, 512-token prompt, 256-token completion, llama.cpp or vLLM backend. GGUF Q4_K_M via llama.cpp or vLLM FP16.

VRAM Usage & Memory Configuration

The 5080 shares the 4060 Ti’s 16 GB VRAM ceiling, so FP16 fits snugly with 1.3 GB to spare. The difference is raw throughput: the 5080 generates tokens more than twice as fast as the 4060 Ti. If your workload needs longer contexts, drop to 4-bit quantisation to unlock additional headroom — at 66.5 tok/s baseline, the speed penalty from longer contexts is more than absorbed by the card’s compute power.

Cost Efficiency: Speed Premium That Pays Off

Despite costing nearly 2x the RTX 4060, the RTX 5080 delivers the lowest cost per million tokens (£3.968) in the Qwen 2.5 7B lineup thanks to its 3x throughput advantage. With batched inference (bs=8), effective cost drops to ~£2.480 per 1M tokens. At 106.4 batched tok/s, the 5080 can serve enough concurrent multilingual requests to make this the most cost-efficient option for medium-traffic production workloads. See our full tokens-per-second benchmark for cross-GPU comparisons.

Best For: Real-Time Multilingual Experiences

The RTX 5080 unlocks use cases that need speed above all else: live translation in video calls, real-time multilingual content moderation, interactive language learning apps, and any scenario where the 15 ms per-token latency translates directly into user delight. If your VRAM needs are modest (8K context or below at FP16), this card offers the best performance-per-pound in the current generation.

Quick deploy:

docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/qwen-2.5-7b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99

For more setup details, see our Qwen 2.5 7B hosting guide and best GPU for Qwen. You can also check all benchmark results, or the LLaMA 3 8B on RTX 5080 benchmark.