Table of Contents
For a small engineering team — say 5 to 15 active developers — the RTX 5060 Ti 16 GB is the cheapest dedicated GPU we host that can run a real coding assistant stack. This page sizes that deployment specifically: which model, what the rest of the stack looks like, and where it stops scaling.
For ~10 active developers, host DeepSeek-Coder 6.7B FP8 + BGE-large embeddings + BGE-reranker on a single RTX 5060 Ti 16 GB. Wire it up to Continue.dev or Tabby. Total cost: £119/mo = £119/seat/month. Covers inline completion + chat + codebase Q&A. For 20+ devs, step up to a RTX 5090 32 GB.
Model choice — fits in 16 GB
16 GB is enough VRAM for a real coding model with headroom for embeddings:
| Model | VRAM (FP8 / INT4) | HumanEval | Best for |
|---|---|---|---|
| DeepSeek-Coder 6.7B | 4 GB FP8 / 4.5 GB INT4 | ~75% | Default pick |
| Code Llama 7B | 7 GB FP8 | ~50% | Older but well-supported |
| Qwen 2.5 Coder 7B | 7 GB FP8 | ~74% | Strong general coding |
| Codestral 22B (INT4) | 12 GB INT4 | ~78% | Best at the edge of 16 GB |
| DeepSeek-Coder V2 Lite 16B (INT4) | 10 GB INT4 | ~80% | Best with INT4 trade-off |
Default recommendation: DeepSeek-Coder 6.7B at FP8. Tiny memory footprint leaves >10 GB free for the embedding stack and KV cache. Slight quality drop vs Codestral 22B but materially faster.
The full coding-assistant stack
Components running on the same 5060 Ti:
- vLLM serving DeepSeek-Coder 6.7B FP8 — ~5 GB
- BGE-large-en embeddings via Text Embeddings Inference (TEI) — ~1.5 GB
- BGE-reranker-v2 — ~1 GB
- Qdrant vector store (CPU + disk) — <1 GB
- LiteLLM router for auth + per-user keys — CPU only
- Caddy with TLS + basic auth — CPU only
Total VRAM: ~7.5 GB. Leaves >8 GB for KV cache and concurrent batching.
How many devs can a 5060 Ti handle?
From customer deployments:
| Concurrent active devs | Behaviour | Recommendation |
|---|---|---|
| 1–5 | Latency excellent, GPU mostly idle | Comfortable |
| 5–10 | TTFT 200–400 ms, occasional queueing | Sweet spot |
| 10–15 | TTFT 400–700 ms, p99 over 1 s sometimes | Workable, watch metrics |
| 15–25 | TTFT degrades, queueing visible | Upgrade to 5090 |
| 25+ | Inline completion latency too high | Upgrade or split traffic |
Note: "active developers" is not the same as headcount. A team of 30 typically has ~10 actively typing at any given moment.
Setup walkthrough
- Order RTX 5060 Ti 16 GB. Provision in <24h.
- Install vLLM and serve DeepSeek-Coder 6.7B at FP8 on port 8000.
- Install Text Embeddings Inference for BGE-large + BGE-reranker on ports 8001 and 8002.
- Install Qdrant on the same host (or a cheap colocated CPU server).
- Run an indexing job over your codebase — typically 30–60 minutes for ~1M LOC.
- Stand up LiteLLM with per-user keys, fronting all three GPU endpoints.
- Each developer installs Continue.dev (VS Code) or Tabby (any editor) and points it at LiteLLM.
- Set up Prometheus alerts on TTFT p99, GPU memory util, and queue depth.
Verdict
The RTX 5060 Ti 16 GB is the cheapest path to a real, dedicated, self-hosted coding assistant. For ~10 developers it lands at £17/seat/month — competitive with GitHub Copilot (£15) when you factor in privacy and customisation. Below 5 devs, just buy Copilot. Above 20, upgrade.
Bottom line
For small teams (5–15 active devs) the 5060 Ti at £119/mo running DeepSeek-Coder 6.7B + embeddings + reranker is the right starting point. For broader cost and tier comparisons see what does it cost to run a self-hosted AI coding assistant?.
