NVIDIA RTX 5080 Hosting — The Latency King
Blackwell at half the price of a 5090. 16 GB of GDDR7, hardware FP4, and the lowest single-stream token latency we measure on any of our cards. The right pick when you need a chatbot that feels instant.
RTX 5080 Server Specs
The hardware you actually rent.
| GPU model | NVIDIA GeForce RTX 5080 (Blackwell, GB203) |
|---|---|
| Architecture | Blackwell — 5th gen Tensor Cores |
| VRAM | 16 GB GDDR7 @ 960 GB/s |
| CUDA cores | 10,752 |
| FP16 compute | ~ 56 TFLOPS |
| FP8 / FP4 | ~ 450 / ~ 900 TOPS |
| TDP | 360 W |
| Host CPU | AMD Ryzen 7 / 9 |
| Host RAM | Up to 64 GB DDR5 |
| Storage | 1 TB NVMe + 4 TB SATA SSD |
| Network | 1 Gbps unmetered |
| Location | London, United Kingdom |
What Fits on a Single RTX 5080
16 GB is the smallest VRAM we’d recommend for production LLM serving. Comfortable for 7B–8B at FP16 and INT8, and capable of 13B at INT4 with shorter context.
| Model | Params | FP16 | INT4 / FP8 | Notes |
|---|---|---|---|---|
| Mistral 7B Instruct | 7B | 14 GB FP16 | 5 GB INT4 | Fits FP16 with 8K context |
| Llama 3.1 8B | 8B | 16 GB FP16 | 5 GB INT4 | Tight FP16 — comfy at FP8 |
| Qwen 2.5 7B | 7B | 14 GB FP16 | 5 GB INT4 | Fits FP16 with 16K context |
| Phi-3 Mini | 3.8B | 8 GB FP16 | 2.5 GB INT4 | Plenty of headroom for long context |
| Gemma 2 9B | 9B | 18 GB FP16 | 6 GB INT4 | FP8/INT4 only — FP16 won’t fit |
| Qwen 2.5 14B | 14B | 28 GB FP16 | 9 GB INT4 | INT4/AWQ only on a 5080 |
| Whisper Large-v3 | 1.5B | 6 GB | n/a | Real-time + headroom for an LLM |
| FLUX.1 schnell | 12B | 24 GB FP16 | 12 GB FP8 | FP8 only on the 5080 |
| SDXL 1.0 | 3.5B | 8 GB FP16 | 4 GB FP8 | Fast and comfortable |
When the RTX 5080 Is the Right Card
Real customer workloads we run on this hardware every day.
Latency-sensitive chatbots
Single-stream Mistral 7B FP8 hits ~95 tok/s on the 5080 — the lowest time-to-first-token of any GPU we host. Great for sub-second customer-facing interactions.
Image generation API
FP8 SDXL and FLUX.1 schnell produce a 1024×1024 in 5–8 seconds. Reasonable for low-volume image APIs and creative tools.
Voice agent backend
Whisper Large-v3 + a 7B LLM + Bark TTS all fit on one card with FP8/INT4 quantisation. Roughly 8 concurrent voice sessions per server.
Embeddings + reranker
Top-end 16 GB embeddings (BGE-large + reranker) at 30K+ docs/sec. A common combo with a 5090 elsewhere doing the LLM work.
Edge inference labs
Closest single-card analogue to what runs on a Jetson Orin or RTX 4080 desktop. Useful when you’re prototyping for edge deployment but want server reliability.
Experimentation / pilots
A team of 5 engineers can comfortably share a 5080 for prototyping. Cheaper than a 5090 and almost as fast on small models.
RTX 5080 vs Other Mid-Range Cards
How this card stacks up against the rest of the GigaGPU catalogue for the workloads we benchmark.
| GPU | VRAM | Throughput / Notes | 70B INT4 fits? | Price |
|---|---|---|---|---|
| RTX 5080 | 16 GB GDDR7 | ~95 tok/s (Mistral 7B FP8 single-stream) | 8B fits, 14B INT4 only | from £189 |
| RTX 5090 | 32 GB GDDR7 | ~92 tok/s single-stream / 1,200 aggregate | 70B INT4 / 14B FP16 | from £399 |
| RTX 4080 | 16 GB GDDR6X | ~75 tok/s | 8B fits, 14B INT4 | from £219 (limited stock) |
| RTX 4070 | 12 GB GDDR6X | ~60 tok/s | 7B INT4 only | from £159 |
| RTX 3090 | 24 GB GDDR6X | ~58 tok/s / 720 aggregate | 13B FP16 fits | from £159 |
Deep Dive
"Latency king" — what we mean
Throughput (tokens/sec, aggregate) and latency (time to first token, time per token) are different metrics. The RTX 5090 wins on aggregate throughput because it has more cores. The RTX 5080 wins on time-per-token in single-stream because it has higher clocks and lower kernel-launch overhead per request.
For a chatbot serving one user per second, either card feels the same. For an agent that needs the model to think in real time at 60+ tok/s with sub-100ms first-token latency, the 5080 is genuinely the best card we host.
Why we still recommend 5090 over 5080 for most teams
The honest answer: 16 GB is enough for one 7B–8B model. 32 GB lets you load a 7B + Whisper + an embedding model on the same card without juggling. Most production stacks end up needing more than one model loaded, and the extra 16 GB on a 5090 pays for itself.
That said, if you know your workload is single-model and latency-sensitive — the 5080 is the right call. Save the difference and put it into a second card later.
FP4 / FP8 paths matter on a 16 GB card
The 5080 has the same Blackwell tensor cores as the 5090. That means hardware FP4, hardware FP8, and the same 2× speedup over FP16 on the right kernels. On a 16 GB card the precision ladder also doubles as a memory-saver:
- Llama 3 8B at FP16 → 16 GB. Tight.
- Llama 3 8B at FP8 → 8 GB. Comfortable, with headroom for KV cache.
- Llama 3 8B at FP4 (NVFP4) → 4-5 GB. You can run a second model alongside.
Most production deployments on a 5080 land at FP8 — best balance of quality, memory, and speed.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Is the 5080 fast enough for a customer-facing chatbot?
Yes — for a single-stream chatbot the 5080 has the lowest first-token latency in our catalogue. For high-concurrency serving (50+ simultaneous users) the 5090 wins on aggregate throughput.
Can I run Llama 3 8B at full FP16?
Just barely with short context. Most teams run it at FP8 (8 GB peak weights + 4–6 GB KV cache) which fits comfortably with a 32K context window.
Is the 5080 enough for fine-tuning?
QLoRA on 7B–13B models works. Full SFT does not — go to a 5090 or 6000 Pro for that.
How does it compare to the 4080?
Blackwell vs Ada — about 25% faster on FP16, 2× faster on FP8/FP4, same VRAM. The 5080 is the better choice unless 4080 stock is meaningfully cheaper.
Can I run two 5080s in one server?
Yes via PCIe. They don’t have NVLink. 2× 5080 = 32 GB combined, which approximates a single 5090 — usually we’d recommend the 5090 instead because tensor parallel adds complexity.
What about the 5070 Ti or 5060 Ti 16 GB?
Lower VRAM (12–16 GB) and lower bandwidth. The 5080 is meaningfully faster on real workloads. We host 5060 Ti as an entry tier — see RTX 5060 hosting.
Power draw at 100% load?
360 W. Easy to cool in our 4U chassis with the standard blower.
Same-day deployment?
Yes for in-stock SKUs. Out-of-stock 5080 lead time is 2–3 working days.
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Latency-sensitive workload? The 5080 is your card.
16 GB GDDR7, hardware FP4, lowest single-stream first-token latency in the catalogue. From £189/mo with same-day deployment.
