Running large language models locally has become increasingly practical in 2026, but choosing the right GPU can make or break your experience. If you’re weighing the RTX 3060, 4060, or 4090 for local LLM inference, you’re asking the right question—but the answer isn’t straightforward. VRAM capacity, not just raw compute power, determines what models you can actually run. Let’s cut through the marketing and look at real-world performance.
Quick Answer: Which GPU Should You Buy?
| GPU | VRAM | 7B Models | 13B Models | 70B Models | Best For |
|---|---|---|---|---|---|
| RTX 3060 12GB | 12GB | ✅ | ⚠️ (Q4 only) | ❌ | Budget builders, 7B experimentation |
| RTX 4060 8GB | 8GB | ✅ | ❌ | ❌ | Casual use, power efficiency priority |
| RTX 4090 24GB | 24GB | ✅ | ✅ | ⚠️ (Q4 only) | Serious LLM users, fine-tuning, RAG applications |
The short version: If you’re on a budget and primarily running 7B models, the RTX 3060 12GB offers exceptional value. The RTX 4060 is surprisingly weak for LLMs despite being newer. The RTX 4090 is the only consumer GPU that handles 13B models comfortably and can run quantized 70B models for serious work.
Understanding VRAM: The Real Bottleneck for Local LLMs
Before we dive into GPU comparisons, you need to understand one critical fact: VRAM is everything for LLM inference. Unlike gaming or traditional GPU workloads where memory bandwidth and compute units matter most, running language models locally is almost entirely constrained by how much video memory you have available.
Here’s why: when you load a language model, the entire model must fit into VRAM. If it doesn’t fit, performance crashes because the system starts swapping to system RAM or disk, creating massive latency spikes that make the model unusable for interactive work.
VRAM Requirements by Model Size
Let’s break down exactly how much VRAM different model sizes require. These numbers are critical for making an informed purchase decision.
7B Parameter Models (Llama 3.1 7B, Mistral 7B, etc.)
- FP16 (full precision): ~14GB VRAM
- Q8 quantization: ~8-10GB VRAM
- Q4 quantization: ~4-6GB VRAM
The 7B models are the sweet spot for most users in 2026. They’re capable enough for coding assistance, writing, analysis, and general chat while being small enough to run on consumer hardware. With Q4 quantization, you can run these models on GPUs with as little as 8GB VRAM, though 12GB gives you more headroom.
13B Parameter Models (Llama 3.1 13B, etc.)
- FP16 (full precision): ~26GB VRAM
- Q8 quantization: ~15-18GB VRAM
- Q4 quantization: ~10-14GB VRAM
This is where things get interesting. The jump from 7B to 13B parameters delivers noticeably better reasoning, instruction following, and output quality. But the VRAM requirements basically eliminate most consumer GPUs from consideration unless you’re willing to use aggressive quantization.
70B Parameter Models (Llama 3.1 70B, etc.)
- FP16 (full precision): 140GB+ VRAM (requires multiple GPUs)
- Q8 quantization: ~70-80GB VRAM (multi-GPU territory)
- Q4 quantization: ~35-40GB VRAM
Running 70B models locally is ambitious. Even with Q4 quantization, you need either a high-end workstation GPU or multiple consumer cards. The RTX 4090’s 24GB VRAM can’t quite handle Q4 70B models comfortably—you’ll be right at the edge or slightly over, depending on context length and batch size.
Why Quantization Matters
Quantization reduces model precision to save VRAM. Q8 uses 8-bit integers instead of 16-bit floating point, roughly halving memory requirements with minimal quality loss. Q4 goes further, using 4-bit quantization to quarter the memory footprint.
The trade-off: Q4 models show more quality degradation than Q8, especially on complex reasoning tasks. For 7B models, Q4 is usually fine. For 13B and up, Q8 is preferable if you have the VRAM budget.
RTX 3060 12GB: The Budget LLM Champion
Here’s something that surprises most people: the RTX 3060 12GB is actually a better LLM card than the newer RTX 4060, despite being an older generation.
Why the RTX 3060 12GB Punches Above Its Weight
The secret is simple: 12GB of VRAM. When NVIDIA released the RTX 3060, they equipped it with 12GB VRAM to compete in the workstation and content creation market. For LLMs, this makes it phenomenally capable at its price point.
What works well:
- 7B models at Q8 quantization: Excellent performance with high quality outputs
- 7B models at Q4: Fast inference with plenty of headroom for long context
- 13B models at Q4: Possible, though you’re at the edge of VRAM capacity
- Multiple simultaneous 7B models: You can actually load more than one small model for comparison
Real-world performance:
- Llama 3.1 7B Q4: ~25-35 tokens/second
- Mistral 7B Q4: ~28-38 tokens/second
- Llama 3.1 13B Q4: ~12-18 tokens/second (tight fit, context length limited)
These speeds are perfectly usable for interactive work. You’re not waiting around for responses. Code completion feels snappy. Writing assistance is responsive.
The Limitations
The RTX 3060 struggles with anything beyond 13B parameters. Even at Q4 quantization, 70B models are completely off the table. You also can’t run 13B models at higher quantization levels—Q8 13B models require more than 12GB, so you’re stuck with Q4’s quality trade-offs.
The other limitation is future-proofing. As models evolve and context windows expand, 12GB will start feeling constrained. If you plan to use this GPU for 3+ years of LLM work, you might outgrow it.
Who Should Buy the RTX 3060 12GB
This card makes sense if:
- Your budget is under $350-400
- You’re primarily running 7B models
- You’re experimenting with local LLMs and aren’t sure of long-term commitment
- You want the best VRAM-per-dollar ratio on the market
- Power consumption and efficiency aren’t major concerns
Current pricing: Used RTX 3060 12GB cards run $250-300. New units are $320-380. At these prices, the value proposition is hard to beat.
RTX 4060 8GB: The Surprising Disappointment
The RTX 4060 represents NVIDIA’s latest architecture with impressive power efficiency and updated tensor cores. For LLMs, though, it’s a step backward from the 3060.
The 8GB VRAM Problem
Eight gigabytes of VRAM in 2026 is limiting for LLM work. You can run 7B models at Q4 quantization comfortably, but that’s about where the capability ends. The architecture improvements don’t compensate for having 33% less memory than the older 3060.
What works:
- 7B models at Q4: Smooth performance
- Very light 7B models at Q8: Possible but tight
What doesn’t work:
- 13B models: Don’t even try
- 70B models: Completely impossible
- Long context windows on 7B: You’ll hit VRAM limits faster
Performance numbers:
- Llama 3.1 7B Q4: ~30-42 tokens/second
- Mistral 7B Q4: ~32-45 tokens/second
The token generation is faster than the 3060 thanks to Ada Lovelace architecture improvements, but the VRAM constraint means you can’t actually run larger or higher quality models to take advantage of that speed.
Power Efficiency: The One Bright Spot
The RTX 4060 draws approximately 115W under load compared to the 3060’s 170W. If you’re running LLMs for extended periods or care about electricity costs and heat output, this efficiency gain is meaningful. Over a year of heavy use, the power savings could amount to $50-80 in electricity costs depending on your rates.
The Verdict on RTX 4060
This is a hard card to recommend for LLM work. You’re paying for newer architecture but getting less capability than the older, cheaper 3060. The only scenarios where the 4060 makes sense:
- You absolutely need low power consumption
- You’re only ever running 7B models at Q4
- You’re building a small form factor system where the 4060’s lower TDP matters
- You already own one and are evaluating whether to upgrade
For most people serious about running LLMs locally, spend the same money on a used 3060 12GB and get meaningfully more capability.
RTX 4090 24GB: The Prosumer LLM Powerhouse
The RTX 4090 is in a different category entirely. With 24GB of VRAM and massive compute throughput, it’s the only consumer GPU that can genuinely handle serious LLM workloads.
What 24GB of VRAM Enables
The jump from 12GB to 24GB isn’t just quantitative—it’s qualitative. You move from being constrained to 7B models to having real flexibility with 13B and even touching 70B territory.
Capabilities:
- 7B models at FP16: Full precision, maximum quality, still responsive
- 13B models at Q8: High quality outputs with good performance
- 13B models at FP16: Possible with shorter context lengths
- 70B models at Q4: Technically feasible but right at the limit
- Fine-tuning 7B models: Actually possible with frameworks like LoRA
- RAG applications: Enough headroom to load models plus vector databases and embeddings
Performance benchmarks:
- Llama 3.1 7B Q4: ~85-110 tokens/second
- Llama 3.1 7B FP16: ~70-95 tokens/second
- Llama 3.1 13B Q8: ~40-55 tokens/second
- Llama 3.1 13B FP16: ~30-42 tokens/second
- Llama 3.1 70B Q4: ~8-15 tokens/second (context dependent)
These speeds transform the user experience. The 4090 delivers responses fast enough that the model feels more like ChatGPT than a local install. Multi-turn conversations stay snappy. Code generation doesn’t make you wait.
Beyond Inference: Fine-Tuning and Development
The 4090’s 24GB VRAM opens up workflows that aren’t possible on smaller cards. You can fine-tune 7B models using LoRA or QLoRA techniques. You can run experiments with different quantization levels. You can load multiple models simultaneously for comparison testing.
For developers building LLM-powered applications, the 4090 provides enough headroom to run your model plus the supporting infrastructure—vector databases for RAG, embedding models, evaluation frameworks, etc.
The Cost Factor
Who Should Buy the RTX 4090
The 4090 makes sense if:
- You regularly work with 13B+ models and quality matters
- You’re fine-tuning or experimenting with model training
- You’re building production RAG applications locally
- You want to run 70B models, even if performance is modest
- Your budget allows $1,600+ for a GPU
- You’re professional or semi-professional in AI/ML work
If you’re casual about LLM usage or primarily stick to 7B models, the 4090 is overkill. Save your money.
Model Size Support: The Detailed Breakdown
Let’s make this crystal clear with a comprehensive support matrix.
| Model Size | RTX 3060 12GB | RTX 4060 8GB | RTX 4090 24GB |
|---|---|---|---|
| 7B Q4 | Excellent | Excellent | Overkill |
| 7B Q8 | Excellent | Tight fit | Excellent |
| 7B FP16 | No | No | Excellent |
| 13B Q4 | Possible | No | Excellent |
| 13B Q8 | No | No | Excellent |
| 13B FP16 | No | No | Limited context |
| 70B Q4 | No | No | Barely possible |
The color coding tells the story: green means smooth performance, yellow means it works but with constraints, red means don’t bother trying.
Power Consumption: The Hidden Operating Cost
Power draw matters more than most buyers realize, especially if you’re running models for hours daily. Let’s look at the real-world power consumption and what it means for your electricity bill.
Power Draw Under LLM Workload:
- RTX 3060 12GB: ~165-175W during inference
- RTX 4060 8GB: ~110-120W during inference
- RTX 4090 24GB: ~420-450W during inference
Annual cost estimate (assuming 4 hours daily use, $0.15/kWh):
The RTX 4060’s efficiency advantage saves about $13/year compared to the 3060—not enough to justify the VRAM trade-off. The 4090’s power consumption is substantial but reasonable given its capability. If you’re running it heavily (8+ hours daily), factor in ~$200/year in electricity costs.
Heat output correlates directly with power draw. The 4090 puts out significant heat and requires good case airflow. The 3060 is manageable with standard cooling. The 4060 runs cool enough for small form factor builds.
Tokens Per Second: Performance That Matters
Raw token generation speed determines how responsive your LLM feels. Here’s what you can expect in real-world usage with popular models.
Llama 3.1 7B (Q4 quantization):
- RTX 3060 12GB: 25-35 tokens/second
- RTX 4060 8GB: 30-42 tokens/second
- RTX 4090 24GB: 85-110 tokens/second
Llama 3.1 13B (Q4 quantization):
- RTX 3060 12GB: 12-18 tokens/second
- RTX 4060 8GB: Not supported
- RTX 4090 24GB: 40-55 tokens/second
Mistral 7B (Q4 quantization):
- RTX 3060 12GB: 28-38 tokens/second
- RTX 4060 8GB: 32-45 tokens/second
- RTX 4090 24GB: 90-120 tokens/second
For context, human reading speed is roughly 4-5 words per second, or 5-7 tokens per second. Anything above 20 tokens/second feels responsive for interactive use. Above 50 tokens/second feels nearly instant.
The 4090’s speed advantage becomes most apparent with longer responses. Generating a 500-token code snippet takes about 18 seconds on a 3060, 14 seconds on a 4060, and under 6 seconds on a 4090. For quick queries, the difference is less noticeable.
Who Should Buy What: The Decision Guide
Let’s cut to the chase with clear recommendations based on your use case and budget.
Buy the RTX 3060 12GB if:
- Your budget is under $400 and you need maximum capability per dollar
- You’re primarily running 7B models for coding assistance, writing, or general chat
- You’re experimenting with local LLMs and aren’t yet committed to heavy usage
- You want flexibility to occasionally run 13B models even if performance isn’t ideal
- You value VRAM over efficiency and don’t mind higher power consumption
The 3060 12GB offers the best value proposition in the market. You get genuine capability with the most popular model sizes at a price point that won’t break the bank. This is the card I recommend to most people getting started with local LLMs.
Where to buy: Used market ($250-300) or new ($320-380). Check r/hardwareswap, eBay, or local retailers. Verify it’s the 12GB variant—the 8GB version exists but isn’t worth buying for LLM work.
Buy the RTX 4060 8GB if:
- Power efficiency is your top priority for a small form factor build
- You’re absolutely certain you’ll never need more than 7B models at Q4
- You’re building a portable LLM workstation where low TDP and heat matter
- You already own one and are evaluating whether it’s worth using
This is the only GPU on this list I struggle to recommend for most users. The VRAM limitation is just too constraining in 2026. If you’re considering buying one new, redirect that $300-350 toward a used 3060 12GB instead.
Buy the RTX 4090 24GB if:
- You regularly work with 13B models and quality/speed matters
- You’re building RAG applications that need room for models plus vector databases
- You’re fine-tuning models locally using LoRA or similar techniques
- You want to experiment with 70B models, even if performance is modest
- Your budget allows $1,600-2,000 without significant financial strain
- You’re professional or semi-professional in AI/ML development work
The 4090 is the only consumer GPU that doesn’t compromise. If you can afford it and you’ll actually use the capability, it’s transformative. Just be honest with yourself about whether you need it—if you’re mainly running 7B models for hobby use, the 3060 will serve you well at a fraction of the cost.
Where to buy: New from retailers ($1,600-1,900) or used from trusted sellers ($1,400-1,700). Be cautious with used 4090s—some have been heavily mined or inadequately cooled.
Conclusion
The best GPU for running LLMs locally in 2026 depends entirely on your budget and ambitions. The RTX 3060 12GB remains the value champion for most users, offering 7B model capability at an accessible price. The RTX 4060 disappoints with limited VRAM despite its newer architecture. The RTX 4090 delivers professional-grade capability for those willing to invest.
For most people starting with local LLMs, I recommend the RTX 3060 12GB. It provides genuine capability without requiring a major financial commitment. If you later discover you need more horsepower, you can upgrade with confidence knowing exactly what you need.