Table of Contents
Long-context LLMs sound like the privilege of bigger GPUs. They aren’t, quite. With FP8 weights and FP8 KV cache, the 5060 Ti 16 GB can host Llama 3.1 8B at full 128K context — single concurrent stream. This page is the precise sizing.
Llama 3.1 8B at FP8 + FP8 KV cache fits 128K context on a 5060 Ti for 1–2 concurrent users. Qwen 2.5 7B fits with similar constraints. For higher concurrency at 128K, step up to a 5090 32 GB.
The KV cache math
For a 7B–8B model with 32 attention heads and 4K head dim, the KV cache per token is roughly 200 KB at FP16, 100 KB at FP8. A 128K context allocates:
- FP16 KV: 128,000 × 200 KB = ~25.6 GB per stream — does not fit any consumer GPU at 8B
- FP8 KV: 128,000 × 100 KB = ~12.8 GB per stream — fits a 5090 with room, fits a 5060 Ti tight
- FP4 KV (experimental): ~6.4 GB per stream — comfortable on 5060 Ti
Plus the model weights: Llama 3.1 8B at FP8 is 8 GB. So:
| Config | Weights | KV cache (1 stream, 128K) | Total | Fits 16 GB? |
|---|---|---|---|---|
| Llama 3.1 8B FP16 + FP16 KV | 16 GB | 25.6 GB | 41.6 GB | No |
| Llama 3.1 8B FP8 + FP16 KV | 8 GB | 25.6 GB | 33.6 GB | No |
| Llama 3.1 8B FP8 + FP8 KV | 8 GB | 12.8 GB | 20.8 GB | Tight, no |
| Llama 3.1 8B FP8 + FP8 KV @ 64K | 8 GB | 6.4 GB | 14.4 GB | Yes |
| Llama 3.1 8B INT4 + FP8 KV @ 128K | 5 GB | 12.8 GB | 17.8 GB | Just over |
| Llama 3.1 8B INT4 + FP8 KV @ 96K | 5 GB | 9.6 GB | 14.6 GB | Yes |
Honest answer: full 128K context on a 5060 Ti requires AWQ-INT4 weights + FP8 KV cache and even then is at the edge of memory. Practical sweet spot: 64K context at FP8 weights, 96K context at INT4 weights.
Models with 128K context that fit 16 GB
| Model | Native context | Recommended config | Practical max context |
|---|---|---|---|
| Llama 3.1 8B | 128K | FP8 + FP8 KV | 64K comfortably, 96K tight |
| Qwen 2.5 7B | 32K (128K w/ YARN) | FP8 + FP8 KV | 64K comfortably |
| Mistral 7B v0.3 | 32K | FP8 + FP8 KV | 32K easily |
| Phi-3 Mini | 128K | FP16 + FP8 KV | 64K comfortably |
| Llama 3.2 3B | 128K | FP16 + FP8 KV | 128K (tiny model) |
vLLM configuration for long context
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
--quantization fp8 \
--kv-cache-dtype fp8_e4m3 \
--max-model-len 65536 \
--max-num-seqs 4 \
--gpu-memory-utilization 0.93 \
--enable-chunked-prefill \
--max-num-batched-tokens 8192 \
--host 0.0.0.0 --port 8000Key flags for long-context:
--enable-chunked-prefill— splits the long prompt into chunks so prefill doesn’t block decode. Critical at 64K+ context.--max-num-seqs 4— bound concurrency tightly; long-context streams eat KV pool quickly.--kv-cache-dtype fp8_e4m3— non-negotiable at 64K+.
Performance at 128K
Llama 3.1 8B AWQ-INT4 + FP8 KV at 96K context, single user:
- Prefill of 96K-token prompt: ~14 seconds (chunked)
- TTFT after prefill: ~250 ms
- Subsequent tokens: ~75 tok/s
The prefill latency at long context is real — feels different from a 4K-context chatbot. For interactive use you want to show the user a "processing your document…" spinner.
Verdict
Yes, the 5060 Ti can run a 128K-context LLM — at INT4 weights, FP8 KV, and limited to ~2 concurrent streams. For real production at 128K, the RTX 5090 32 GB is the right home: same model at FP8 weights with comfortable concurrency. For the 5060 Ti, the practical recommendation is cap at 64K context and use it as a chatbot host, not a document-analysis backend.
Bottom line
16 GB just barely supports 128K context on an 8B model. If long-context is genuinely your workload, this is the wrong card — get a 5090. If you can constrain to 32K–64K, the 5060 Ti at £119/mo is the cheapest path. For broader LLM sizing across our catalogue, see best GPU for LLM inference.
