What Can You Actually Run on 16GB VRAM?

📚 More on this topic: VRAM Requirements · What Can You Run on 12GB · What Can You Run on 24GB · Quantization Explained

You have 16GB of VRAM. Maybe it’s an RTX 5060 Ti, a 4060 Ti 16GB, a 4080 Super, or an AMD RX 7800 XT. You know 16GB is more than 12GB. The question is: how much more?

The honest answer: meaningfully more, but not as much as you might hope. 16GB is the awkward middle child of local AI — clearly better than 12GB, clearly behind 24GB, and in a strange position where the models that fit well are excellent but the next tier up is just out of reach. This guide covers exactly what that means in practice.

Who This Is For

These cards share the same VRAM ceiling but have very different performance. The RTX 4080 Super generates tokens roughly 2.3x faster than the 4060 Ti 16GB thanks to bandwidth — they just happen to load the same models. The RTX 5060 Ti 16GB sits in between, offering 50% more bandwidth than the 4060 Ti for nearly the same price.

If you’re choosing between these cards, bandwidth matters more than you’d think for LLM inference. But VRAM capacity determines what you can run at all, and that’s what this guide focuses on.

The Awkward Middle Tier

Here’s the honest positioning of 16GB:

What you gain over 12GB:

• 13B-14B models at Q6-Q8 instead of just Q4
• 20B-22B models fit (they don’t at 12GB)
• Longer context windows — 16K+ on 14B models vs. 8K at 12GB
• Flux image generation at FP8 with actual headroom (12GB is razor-thin)
• SDXL with base + refiner comfortably, with room for LoRAs

What you still can’t do that 24GB can:

• Run 27B-32B models (weights alone exceed 16GB at Q4)
• Use 70B models at any usable quality
• Fine-tune anything larger than ~7B
• Run multiple large models simultaneously
• Use Flux at full FP16 precision

Sixteen gigabytes is where you stop squeezing 7B-8B models and start running genuinely larger ones. It’s not where you run everything — that’s 24GB.

What Runs Well on 16GB

7B-8B at Q8/FP16: Maximum Quality

With 16GB, you can stop quantizing 7B-8B models aggressively and run them at near-original quality. At Q8, a 7B model uses ~8GB for weights, leaving 8GB for the KV cache — enough for 32K-65K+ token context windows.

At Q8, quality is essentially lossless — perplexity increase is roughly 0.01 points versus FP16. This is where 16GB shines for 7B models: no quality compromise and massive context windows.

FP16 fits but leaves almost no headroom. Use it only if you need bit-perfect output and can live with short context.

13B-14B at Q4-Q8: The Sweet Spot

This is the reason to own 16GB. The 13B-14B class is a major quality jump over 7B-8B, and 16GB handles it comfortably at Q4-Q6 — something 12GB can do at Q4 but struggles with at higher quants.

The sweet spot here is Q4_K_M with 8K-16K context. That gives you the best balance of quality, speed, and usable context length. If you want higher quality and can accept shorter conversations, Q6 is excellent — and it’s something 12GB cards can’t do comfortably with 14B models.

20B-22B at Q4: The Upper Limit

This is what 16GB unlocks that 12GB can’t touch. Models like Codestral 22B and MoE architectures become accessible:

Codestral 22B at Q4 is a highlight. It’s one of the strongest open-source coding models, and 16GB is the minimum to run it on GPU. Context is limited to a few thousand tokens, so it’s best for code completion and function generation rather than processing entire codebases.

MoE (Mixture-of-Experts) models deserve special mention. They have high total parameters but only activate a fraction per token. The GPT-OSS 20B runs at 82 tok/s on the RTX 5060 Ti at 16K context — faster than dense 14B models despite being “bigger.”

What’s Possible But Painful

32B at Q3: Technically Loads, Barely Usable

A 32B model at Q4_K_M needs ~19-20GB — it doesn’t fit. At Q3_K_S (~14GB) or IQ3_M (~13-14GB), the weights fit, but you’re left with 2-3GB for the KV cache.

That means:

• Context limited to roughly 1K-4K tokens
• Quality degraded by aggressive quantization
• Speeds of ~10-15 tok/s (GPU-only) or ~5-10 tok/s (with CPU offloading)

Verdict: A 14B model at Q4-Q6 will produce better output, faster, with longer context. The 32B squeeze is a party trick, not a daily driver. For real 32B inference, you need 24GB.

70B: Not Happening

Even at the most aggressive 2-bit quantization (IQ2_XS), a 70B model’s weights approach 15-17GB — leaving nothing for context. With heavy CPU offloading, you might see 1-3 tok/s at severely degraded quality.

A Qwen 2.5 14B at Q4 will beat a 70B at Q2 on most tasks and run 10x faster. Don’t chase parameter counts when the quantization destroys the quality advantage.

What Won’t Work

• 32B+ models at Q4+: Weights alone exceed 16GB. Not without heavy CPU offloading.
• 70B models: Even at Q2, it’s not practical.
• Fine-tuning 14B+ models: LoRA on 7B fits at FP16. LoRA on 14B is tight. Anything larger needs 24GB.
• Multiple models simultaneously: One large model at a time. Loading a second will OOM.
• 128K+ context on 14B: The KV cache alone would need more than 16GB.

Image Generation on 16GB

16GB is a genuine upgrade over 12GB for image generation. Several models that are tight or impossible at 12GB become comfortable.

SDXL: Comfortable

SDXL base uses ~7-8GB. On 16GB, you can run base + refiner simultaneously (~10-12GB total) with room left for LoRAs. No memory-management hacks needed — just generate.

Flux: FP8 Is the Move

Flux Dev at FP16 needs ~24GB — it won’t fit. At FP8, it drops to ~12GB and runs well on 16GB with headroom to spare.

FP8 delivers nearly identical quality to FP16 with ~40% faster generation. Flux Schnell at FP8 is particularly impressive — 1-4 steps for near-instant images. On 12GB, Flux FP8 works but with zero headroom; 16GB gives you room to breathe.

SD 3.5 Large: Fits with Quantization

SD 3.5 Large at full FP16 needs ~18GB — too much. But with NVIDIA’s TensorRT FP8 optimization, it drops to ~11GB with minimal quality loss and a 2.3x speed boost. GGUF Q8 versions also fit at ~16GB.

This is a clear 16GB advantage over 12GB: SD 3.5 Large at Q8 simply doesn’t fit on a 12GB card. For the best Stable Diffusion output quality available, 16GB is the practical minimum.

Best Models for 16GB GPUs (Ranked)

1. Qwen 2.5 14B Instruct — The all-rounder for this tier. Strong at everything, excellent multilingual and coding. This is your default.

ollama pull qwen2.5:14b

2. Llama 3.1 8B Instruct (Q8) — When you want maximum quality on a smaller model with long context. Q8 on 16GB gives you lossless quality and 32K+ context.

ollama pull llama3.1:8b

3. DeepSeek R1 14B — Best for reasoning and math at this tier. Uses chain-of-thought to improve complex answers.

ollama pull deepseek-r1:14b

4. Codestral 22B — The best coding model you can run on 16GB. Context is limited (~2-4K), so use it for function-level work rather than full-file analysis.

ollama pull codestral:22b

5. Phi-4 14B — Microsoft’s strong reasoning model. Competitive with Qwen 2.5 14B on benchmarks, slightly better on some reasoning tasks.

ollama pull phi4:14b

6. Gemma 3 12B — Google’s efficient option. Strong instruction following and a slightly smaller footprint than 14B models, giving more context headroom.

ollama pull gemma3:12b

New to local AI? Ollama handles everything — one command to install, one to run. If you prefer a visual interface, LM Studio works just as well.

Tips to Maximize 16GB

1. Pick the Right Quantization

On 16GB, you have real choices:

Don’t default to Q4 for everything. If the model is small enough, use Q6 or Q8. The quality difference is noticeable, and you have the VRAM for it.

2. Use Flash Attention

Flash Attention reduces KV cache memory usage significantly. Most modern inference engines (llama.cpp, vLLM) support it. On 16GB, this can mean the difference between 8K and 16K context on a 14B model.

3. Try KV Cache Quantization

Newer versions of llama.cpp support Q8 and Q4 KV cache, which roughly halves or quarters the memory used by context. With Q8 KV cache on a 14B model at Q4 weights, you can push context from ~16K to ~32K tokens.

# llama.cpp with Q8 KV cache
./llama-server -m model.gguf -ctk q8_0 -ctv q8_0

4. Close VRAM Hogs

The same advice from every other tier: Chrome’s hardware acceleration, video players, and game launchers consume VRAM. Close them before loading large models.

5. Monitor VRAM Usage

nvidia-smi -l 1

If you’re hovering near 15-16GB, you’re on the edge. Drop one quantization level or reduce context length.

Is 16GB Worth It? (Honest Take)

This is the question everyone with a 16GB card — or considering one — needs answered.

16GB vs. 12GB: What You Gained

The jump from 12GB to 16GB is real but incremental. You don’t unlock a new class of models the way 24GB does — you get more headroom on the same class (13B-14B) and access to a few larger models (20B-22B) with tight context.

16GB vs. 24GB: What You’re Missing

The jump from 16GB to 24GB is the bigger one. Eight extra gigabytes unlocks 27B-32B models — the quality tier where local models start competing with commercial APIs. If your budget allows it, 24GB is the better investment.

When 16GB Is the Right Call

• You already own a 16GB card. Don’t upgrade for the sake of upgrading. 16GB runs excellent 14B models at interactive speeds.
• You want a new card under $500. The RTX 5060 Ti 16GB at ~$429 is the best value in this range. A used RTX 3090 is better for AI but costs more, draws 350W, and is five years old.
• You need a card for gaming AND AI. The 5060 Ti and 4080 Super are excellent gaming cards. A used 3090 is powerful but loud, hot, and huge.
• You run 7B-14B models daily. These run perfectly at 16GB with the quality and context you need. If you’re not constantly reaching for 32B+ models, 16GB is enough.

When 16GB Is Not Enough

• You need 32B+ models. Qwen 2.5 32B, DeepSeek R1 32B, Gemma 3 27B — these need 24GB.
• You want to fine-tune models. LoRA on 14B barely fits at 16GB. Real fine-tuning needs headroom.
• You run complex image generation workflows. Multi-model ComfyUI pipelines with ControlNet, IP-Adapter, and LoRAs can exceed 16GB.
• You want to future-proof. Models keep getting bigger. 16GB is comfortable today but may feel tight in a year.

When to Upgrade (And What To)

You’ve outgrown 16GB when:

• You’re constantly choosing smaller models or lower quants to fit
• You need 32B models for your work
• You want longer context on 14B+ models
• Image generation workflows keep hitting OOM

The upgrade path:

The used RTX 3090 remains the value play. For the price of two 16GB cards, you get 24GB of VRAM and 936 GB/s of bandwidth — over 3x what the 4060 Ti 16GB offers. If AI work is your priority, it’s the best dollar-per-capability upgrade available.

The Bottom Line

16GB is a capable, slightly awkward tier. It runs 13B-14B models at quality levels that 12GB can’t match, handles Flux and SDXL without workarounds, and gives you just enough room for 20B-22B models in a pinch. It doesn’t reach 32B, and it doesn’t pretend to.

The practical advice:

1. Install Ollama and pull qwen2.5:14b. That’s your workhorse at Q4 with 8K-16K context.
2. Run 7B-8B models at Q8 instead of Q4 — you have the VRAM, use it for better quality.
3. Try Codestral 22B for coding tasks. It’s tight but worth the squeeze.
4. When 16GB isn’t enough — and 32B models are calling — a used RTX 3090 is the move.

Your card is more capable than 12GB and honest about not being 24GB. Use what it does well.

Related Guides

• What Can You Actually Run on 12GB VRAM?
• What Can You Actually Run on 24GB VRAM?
• GPU Buying Guide for Local AI
• What Quantization Actually Means

Sources: Hardware Corner GPU Ranking for LLMs, Hardware Corner RTX 5060 Ti Analysis, LocalScore.ai RX 7800 XT, Puget Systems LLM Inference Benchmarks, Civitai Flux FP8 vs FP16, BestValueGPU Price Tracker