Ollama on RTX 4060 Ti 16GB

16GB is where local inference gets comfortable: stronger models, better context flexibility, and fewer abrupt spills than 8GB setups. You still need deliberate context and concurrency control to stay fully on GPU.

For most users, 8B to 14B models are the practical quality zone on this card class.

16GB gives breathing room, but it does not remove the spill cliff. You still need to pick where you want that extra memory to go: bigger model, bigger context, or safer concurrency.

What 16GB Fixes and What It Does Not

Compared to 8GB, 16GB dramatically improves day-to-day stability for 12B to 14B classes. Compared to 24GB, it still needs more careful context discipline when chats get long or multiple requests run at once.

If a setup feels great in a short benchmark but drifts later, that usually means the combined cost of context growth and background overhead crossed your headroom line.

Strong 16GB Model Picks

Model	Best for	Starting quant	Starting context
Llama 3.1	General assistant and tools	Q5 to Q6	8K
Gemma 2	Chat and summarization	Q5 to Q6	8K
Mistral NeMo	Balanced code + reasoning	Q4 to Q5	8K
Qwen2.5	Multilingual long-form	Q4 to Q5	4K to 8K
Qwen2.5 Coder	Coding and refactoring	Q4 to Q5	4K to 8K
Phi-4	Instruction quality	Q4 to Q5	4K to 8K
Phi-4 Reasoning	Hard reasoning tasks	Q4 to Q5	4K to 8K
DeepSeek-R1	Reasoning-heavy prompts	Q4 to Q5	4K

The strongest practical pattern on 16GB is to stay in 8B to 14B and spend your remaining budget on context and stability instead of chasing bigger checkpoints.

If you need vision workflows, test Llama 3.2 Vision with smaller context first.

Context Strategy on 16GB

Ollama defaults GPUs under 24 GiB to 4K context. On 16GB, that is usually the right launch profile for 14B models before moving upward.

Goal	Suggested `num_ctx`	Model range
Low-latency chat/coding	4096	12B to 14B
Longer sessions	8192	8B to 12B, sometimes 14B
Long documents	16384	Prefer smaller models

For 14B coding models, 4K is the safest default. Move to 8K only after confirming latency and throughput stay consistent over longer sessions.

Concurrency Can Trigger Unexpected Spill

Context per request	Parallel requests	Effective allocation
4096	1	4096
4096	2	8192
4096	4	16384

If performance collapses in server mode but not in single-chat mode, concurrency is often the hidden reason.

Upgrade Path Without Instability

Step 1: choose the model family you trust (general, coder, reasoning).
Step 2: lock num_ctx=4096 and validate long-session behavior.
Step 3: increase to 8192 only if task quality improves materially.
Step 4: scale parallelism last, after single-session stability is proven.

Practical 16GB Rules

Use 4K context as default for 14B models and test upward in steps.
Protect KV cache headroom before chasing bigger model sizes.
Reduce context before lowering quant when latency degrades.
Leave VRAM margin for desktop overhead, especially on Windows.

References

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