Ollama on 24GB GPUs (RTX 3090 / 4090)

24GB is a major local-LLM threshold: enough for stronger models and serious context windows while staying on a single consumer GPU. The biggest gains come from explicit context control, not leaving every model at the same default.

The qualitative jump from 16GB is that context becomes a real working tool, not just a risk to minimize. You can keep richer chat history and larger prompt packs without immediately forcing offload.

Ollama Context Defaults by VRAM Tier

Detected VRAM tier	Default context
Under 24 GiB	4K
24 to 48 GiB	32K
48 GiB or more	256K

On 24GB cards, default 32K context is powerful but expensive. Use it when needed, not by habit.

Many users hit this exact trap: load a larger model, forget default context is 32K, then wonder why CPU usage rises. The fix is usually to lower context before changing the model family.

Model Picks That Map Well to 24GB

Model	Size class	Best for	Starting profile
Llama 3.1	8B	General assistant	Q6 to Q8, 16K to 32K
Gemma 2	9B	Chat and summarization	Q6 to Q8, 16K to 32K
Mistral NeMo	12B	Balanced code + reasoning	Q5 to Q6, 16K to 32K
Qwen2.5 Coder	14B	Coding	Q5 to Q6, 16K to 32K
Qwen2.5	14B	Multilingual long-form	Q5 to Q6, 16K to 32K
DeepSeek-R1	14B	Reasoning	Q5 to Q6, 16K to 32K
Llama 3.2 Vision	11B vision	Vision + text	Q5 to Q6, 8K to 16K

32B-class workloads can fit on 24GB with lower quantization and tighter context, but 14B-class models usually deliver better day-to-day responsiveness unless you explicitly need the larger model’s output behavior.

RTX 3090 vs RTX 4090 for Ollama

Aspect	RTX 3090	RTX 4090	Practical effect
VRAM capacity	24GB	24GB	Similar model fit limits
Prompt + generation speed	Good	Higher	4090 usually feels more responsive
Value profile	Cost-efficient 24GB entry	Top single-GPU performance	Pick by budget vs latency target

In practice, both cards run similar model sets because capacity is equal. The 4090 usually wins on throughput and latency, while the 3090 often wins on value.

How People Accidentally Spill on 24GB

Leaving default 32K context enabled for every workload.
Adding parallel API requests without re-checking effective context allocation.
Running desktop-heavy workloads while benchmarking close-to-limit model settings.
Jumping to larger model tiers before validating long-session stability.

24GB Stability Rules

Use 8K to 16K for low-latency chat and coding defaults.
Move to 32K when long history or many files actually improve quality.
For 32B-class models, lower context first to protect KV cache headroom.
In server mode, treat parallel requests as a direct memory multiplier.

References

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