Running Llama 70B Locally: Comparison and Selection Guide for 5700XT, Mac M4, and CUDA Solutions

Want to run Llama 70B locally? Is your AMD 5700XT with 8GB VRAM enough? Can Mac M4 handle it?

The answer might surprise you. The 70B model FP16 full version requires 140GB VRAM—basically impossible for consumer hardware. But quantization technology has lowered the threshold to around 40GB, making things suddenly interesting.

This article will use real test data to compare three common solutions: AMD 5700XT (tinkerer’s favorite), Mac M4 (killer advantage of unified memory), and NVIDIA CUDA (mature ecosystem veteran). After reading, you’ll be able to judge which one suits you in about 5 minutes.

The Truth About Llama 70B’s VRAM Requirements

Quantization, simply put, is “compressing” the model. The original FP16 version has each parameter occupying 2 bytes. Multiply 70 billion parameters—that’s 140GB VRAM. Even with an RTX 4090’s 24GB, it’s still not enough.

So what can you do? GGUF format quantized versions are here.

Which Quantization Level to Choose?

Different quantization levels have vastly different VRAM usage:

Quantization Level	VRAM Required	Accuracy Loss	Use Case
Q8_0	~75GB	Minimal	Research experiments, pursuing accuracy
Q6_K	~55GB	Low	Have 64GB+ memory
Q5_K_M	~45GB	Acceptable	Mac 64GB memory
Q4_K_M	~35-40GB	Balanced	Most consumer hardware
Q3_K_M	~30GB	Noticeable	Extreme VRAM compression

I recommend Q4_K_M. Why? This level finds a nice balance between accuracy and VRAM. You might have heard Q3 can run too, but the accuracy loss is quite noticeable—response quality drops, reasoning ability is compromised. Q5 and above are certainly better, but VRAM requirements go up again.

There’s one more thing to remember: KV Cache. During inference, the model needs to store context information, which takes an additional 5GB or so. So to actually run the Q4_K_M version, you need about 40-45GB of available memory space.

Real-World Hardware Comparison

Let’s look at the table directly. Data comes from Reddit LocalLLaMA forum and several tech blog benchmark reports.

Solution	VRAM/Memory	Runnable Models	70B Q4 Performance	Price Range	Setup Difficulty
AMD 5700XT	8GB VRAM	7B fully, 12B partial	Not recommended	Used $150-200	Difficult
Mac M4 Max	128GB unified memory	70B Q4/Q5	20-28 tok/s	$3500+	Easy
NVIDIA RTX 4090	24GB VRAM	32B fully, 70B offload	18 tok/s (offload)	$1500-2000	Medium
NVIDIA RTX 5090	32GB VRAM	70B Q4 single card	Estimated 25+ tok/s	$2000+	Easy

AMD 5700XT: The Tinkerer’s Nightmare

To be honest, running 70B models on 5700XT is basically “gritting your teeth and doing it.” With 8GB VRAM, even 7B Q4 barely fits, and 70B is completely out of the question. But some people just won’t give up—I’ve tried ROCm workaround solutions myself.

The result? Unstable. You can get it running, but it might crash at any moment. AMD officially doesn’t support ROCm for RDNA1 architecture (which 5700XT belongs to), relying on environment variable overrides created by the community:

HSA_OVERRIDE_GFX_VERSION=10.1.0

This trick can fool ROCm into running, but performance is mediocre and stability is poor. If you just want to tinker and learn, give it a try. For serious use? Forget it.

Mac M4: Unified Memory is the Killer Feature

Apple Silicon’s unified memory architecture is simply brilliant for running large models. With 128GB M4 Max, system memory and VRAM are the same—you don’t have to worry about “VRAM not enough, need to offload to memory.”

Real test data is impressive: 20-28 tok/s. This speed is quite comfortable for local inference. And setup is simple—install Ollama or use MLX directly, a few commands and you’re running.

The only issue is price. M4 Max starts at $3500+, not a small sum. But if you already need a Mac for other work and can run large models on the side—the calculation works out.

NVIDIA CUDA: Mature Ecosystem, But Large Models Need Offload

RTX 4090’s 24GB VRAM is more than enough for 32B models. For 70B? Not enough. You need to use offload solutions—some layers on GPU, the rest in system memory.

This works, but speed drops. Real tests show around 18 tok/s, slower than Mac M4 Max. Because moving data back and forth between CPU and GPU takes time.

RTX 5090’s 32GB VRAM is better, 70B Q4 can run on a single card. However, this card isn’t officially released yet, price estimated starting at $2000.

CUDA’s advantage is mature ecosystem. Want to fine-tune models? NVIDIA’s toolchain is most complete. PyTorch, Hugging Face all prioritize CUDA support. This is something Apple Silicon and AMD can’t match.

How to Determine Which Solution Fits You

Don’t overthink it, follow this process step by step:

Step 1: Check What You Already Have

Already have 5700XT?

• Can try ROCm workaround, but be prepared for tinkering
• Actually can only run 7B models (12B requires partial offload)
• Suitable for those who want to learn ROCm principles and are willing to troubleshoot

Already have Mac?

• Check memory size: 64GB can run 70B Q5, 128GB is more comfortable
• M4 Pro/Max perform better, M4 base model works too
• Just try it, high success rate

Have nothing?

• Check budget situation below

Step 2: Budget Determines Choice

Budget Range	Recommended Solution	Notes
<$500	Used 5700XT or Mac Mini M4 entry	5700XT is risky, M4 entry 16GB memory only runs small models
$500-2000	RTX 4090 or Mac Mini M4 Pro	RTX 4090 needs offload, M4 Pro 24GB memory sufficient for 70B
$2000+	RTX 5090 or Mac Studio M4 Max	Depends if you need fine-tuning/training—fine-tuning choose NVIDIA, pure inference choose Mac

Step 3: What Do You Want to Do?

Just want to try and play?

• Any hardware that can run 7B is enough. No need to struggle with 70B, small models can give you the local inference experience.

Daily use, need stability?

• Mac M4 series is most worry-free. Install software and use, no need to worry about CUDA versions, ROCm configurations.

Need fine-tuning/training?

• NVIDIA CUDA is the only choice. Most complete ecosystem support, most tutorials, fewest pitfalls.

Pursuing ultimate inference speed?

• Mac M4 Max MLX acceleration is 30-50% faster than llama.cpp, will explain in detail later.

Actually, most people fall into the second category—daily use with stability. Mac has a clear advantage here. You don’t need to tinker with graphics drivers, don’t worry about compatibility issues, works out of the box.

Mac Users’ MLX vs llama.cpp Choice

Mac users have an extra decision point: MLX or llama.cpp?

Performance Comparison

According to Compute Market’s real test data:

Scenario	MLX	llama.cpp	Difference
Short prompt (<512 tokens)	Faster	Baseline	MLX 30-50% faster
Long prompt (>2048 tokens)	Baseline	Faster	llama.cpp slightly better
Overall inference speed	~25 tok/s	~20 tok/s	MLX leads

MLX is a framework Apple specifically optimized for Silicon chips, can directly call Metal GPU acceleration. llama.cpp is a cross-platform solution, although it also supports Metal, but not to the extent of MLX.

How to Choose?

Pure inference, pursuing speed?

• Use MLX. Just mlx_lm.generate command to run, simple setup, fast speed.

Need llama.cpp toolchain compatibility?

• If you want to use certain third-party tools that depend on llama.cpp, or migrate the same GGUF file between different devices—then llama.cpp. It has better compatibility, can run on almost all platforms.

Not sure?

• Try both. Installation isn’t complicated anyway, run it and you’ll know which fits your usage habits better.

I personally lean towards MLX. My main use case is local inference anyway, speed is fast enough. Toolchain compatibility isn’t a must-have for me.

Summary

After all this, here’s a quick decision table for you:

Your Situation	Recommended Solution	Reason
Already have Mac (64GB+ memory)	Use directly, choose MLX	Most worry-free, good speed
No hardware, budget <$500	Mac Mini M4 entry	More stable than 5700XT, lower risk
Budget $500-2000, need stability	Mac Mini M4 Pro	24GB memory sufficient for 70B
Budget $2000+, need fine-tuning	RTX 4090/5090	CUDA ecosystem mature
Want to tinker and learn ROCm	Used 5700XT	Cheap, but be prepared for pitfalls

Core conclusion in one sentence: Mac is worry-free and stable, CUDA has comprehensive ecosystem, AMD offers high price-performance but more tinkering.

If your need is “serious use” and don’t want to spend time tinkering with configurations—choose Mac. If budget is tight and willing to troubleshoot—5700XT can be tried, but don’t have high expectations for 70B. If you need model fine-tuning—NVIDIA CUDA is the only choice.

Ready to try? If you have a Mac, you can install Ollama or MLX directly and run a 7B model to experience it. Without a Mac, first check if your existing hardware can run small models—70B isn’t the starting point, run something first then talk.