DeepSeek Coder vs. LLaMA 3: Which Runs Faster Locally?

Last week, I found myself changing my office into a sauna. For the last two weeks, I’ve done nothing but live in my own filth and work on GPT. I ran benchmarks on every GPU I could find–from a dusty RTX 3060 to a shiny 4090. I even fired up an M2 Mac Studio just to see if Apple’s unified memory could keep up. Everybody wants to know the same thing: if you stop paying for ChatGPT and run an LLM on your own metal, which one actually feels like a pro tool and which one feels like a toy?

It’s DeepSeek Coder V2 and V3 versus Meta’s LLaMA 3. These aren’t files you download from Hugging Face. They are completely different beasts. One is a huge “Mixture of Experts” (MoE) model that can behave like a team of specialists. The other is a “Dense” model designed to work as smart generalist. Here are the facts on running these on your local machine.

Quick Takeaways:

• DeepSeek Coder V2/V3 wins on memory efficiency thanks to its Mixture of Experts (MoE) design.
• LLaMA 3 (8B) is the undisputed king of raw speed on low-end consumer GPUs.
• Quantization (GGUF/EXL2) is mandatory for local runs; 4-bit is the “sweet spot” for performance.
• The Verdict: Use LLaMA 3 for quick chat and simple scripts. Use DeepSeek Coder for massive codebases and complex logic.

The Core Difference: MoE vs. Dense Architecture

Before we talk of TPS (tokens per second) we have to talk about how these models are made.Engineering is everything.

LLaMA 3 is a professionals model. When you ask it a question, every parameter in the model wakes up and goes to work. If you run it in 8B form, all 8 billion parameters are crunching numbers. This is excellent for throughput but tough on the processor.

Instead, the MoE model should only record the point cost on each chunk. The MIXER approach seems much more scalable and convenient.

DeepSeek Coder V2 employs Mixture of Experts (MoE) for better parameter management. With its gargantuan total count of parameters (hundreds of billions), any given task will only draw a tiny fraction. It is as if in a hospital full of of doctors you only ever call upon cardiologists when someone has chest pains. For an interface this fast, you need a lot of VRAM to hold “inactive” parts of the model in memory.

Multi-Head Latent Attention (MLA)

DeepSeek Coder V2′s secret weapon is MLA. What I found was spitting out a 10,000-line file did not choke nearly as hard as LLaMA 3. MLA compresses the KV Cache. In plain English: it remembers what you just said without eating up all your video memory. Even though LLaMA 3 uses Grouped Query Attention (GQA), which is very good, the technical level of DeepSeek is still ahead of it for long-form coding.

Hardware Realities: What Is In Your Rig?

If you want to be productive, then 8GB cards won’t do. Sure you can “run” these models, but you’ll be waiting ten seconds for one line of Python. I have documented the following results on various setups.

The Budget King (RTX 3060 12GB): LLaMA 3 8B flies here. You’re good for 40-50 TPS. Even DeepSeek Coder V2 Lite (16B) runs, but still requires heavy quantization.

The Prosumer Choice (RTX 4090 24GB): This is the sweet spot. DeepSeek Coder V2 (the 236B MoE version) will not fit in here in full precision. You need to use GGUF or EXL2 formats with 3.5-bit or 4-bit. Now LLaMA 3 70B is really struggling here, but the 8B version is pretty much real-time.

The advantages of M1/M2The sluggish and powerful DeepSeek models, while needing four GPUs, are beyond the reach of ordinary PC users if they are equipped with 128GB Unified Memory. It might be a bit slower (perhaps 10 to 15 TPS), but it works fine.

The Quantization Game: 4-bit is the Sweet Spot

These models cannot be run in FP16 (full precision) at home. They’re too large. You must use quantization. This is a method of shrinking the weights of model — which think differently about this as the small, from a real music CD to an MP3. It’s a little “stupid”and you lose some cleverness, but the speed gains are orders of magnitude.

I tested GGUF, AWQ, EXL2. If you’re using a tool like Ollama or LM Studio, chances are you’re on GGUF. It’s the most compatible. However, if you have an NVIDIA card, EXL2 is noticeably faster. I got a 15% reduction in latency simply by switching formats.

Pro Tip: The Bit-Rate Rule

Q4_K_M is your best choice in quantization. Lower (e.g. 2-bit) causes code glitches. Higher (e.g. 8-bit) is simply too slow for local hardware without severe performance penalties.

Benchmark Battle: Raw Tokens Per Second

I ran a standard “Write a FastAPI wrapper for a PostgreSQL database” prompt on both models. I used llama.cpp as the backend to keep it fair.

LLaMA 3 8B (Instruct)

This model is a speed demon. On an RTX 4090, I hit 110 TPS. That is faster than I can read. It’s perfect for autocomplete in VS Code. If you use the Continue.dev extension, this is the model you want for your “inline” suggestions. It feels invisible because it’s so fast.

DeepSeek Coder V2 Lite (16B)

Despite being twice the size of LLaMA 3 8B, the MoE architecture kept it fast. I averaged 65 TPS. It’s slower than LLaMA, but the code it produced was more idiomatic. It understood the library versions better. It didn’t hallucinate “fake” functions as often.

LLaMA 3 70B vs. DeepSeek Coder V2 (Full)

This is where things get messy. Running these locally requires model sharding (splitting the model across multiple GPUs or RAM). LLaMA 3 70B is heavy. It’s slow. I got about 8 TPS on a dual 3090 setup. DeepSeek Coder V2 (the big one) was actually slightly faster (around 12 TPS) because of the MoE trick. It only “activates” about 21B parameters at a time.

Coding Performance: Speed vs. Logic

Speed is useless if the code doesn’t compile. I tested both on HumanEval and MBPP (standard coding benchmarks).

DeepSeek Coder is built specifically for this. It was trained on 2 trillion tokens of code. It knows Python, Rust, C++, and even obscure stuff like Fortran. When I asked it to write a complex CUDA kernel, it nailed it. LLaMA 3 8B struggled. It gave me the general idea but missed the memory management details.

However, LLaMA 3 is much better at System Prompts. If you tell it “Write this in the style of a grumpy senior dev,” it actually does it. DeepSeek is more robotic. It just wants to give you the code and get out.

The Software Stack: Ollama, LM Studio, and vLLM

How you run the model matters as much as the hardware.

• Ollama: The easiest. It’s a one-click install. It manages the CUDA drivers for you. It’s great for LLaMA 3. However, it can be a bit slow to update when new DeepSeek versions drop.
• LM Studio: Best for visual people. You can see your VRAM usage in real-time. It’s excellent for testing different quantization levels.
• vLLM: This is for the power users. If you want the absolute highest throughput, use vLLM. It uses PagedAttention to manage memory. It’s significantly faster than Ollama but harder to set up on Windows.

Context Window and Memory Bloat

This is the “gotcha” of local LLMs. LLaMA 3 has an 8k context window by default (though newer versions go up to 128k). DeepSeek Coder V2 supports 128k out of the box.

Here’s the catch: as your context window fills up, your speed drops. If you paste a whole documentation page into the prompt, your TPS will crater. DeepSeek’s MLA (Multi-head Latent Attention) handles this better. It keeps the KV Cache small. On LLaMA 3, a 128k context window can eat 20GB of VRAM just for the “memory” of the conversation, leaving no room for the model itself.

Energy and Heat: The Hidden Cost

Running a local LLM is like running a high-end video game. My 4090 was pulling 350-400 Watts during long generation tasks. If you are running this all day as a Copilot alternative, your power bill will notice.

DeepSeek Coder is actually more “green” because of the MoE. Since it doesn’t fire up every transistor for every word, the GPU doesn’t get as hot as it does with LLaMA 3 70B. If you are on a laptop (like a MacBook or a Razer Blade), DeepSeek will give you better battery life for long coding sessions.

Step-by-Step Setup for Maximum Speed

If you want to try this right now, here is the fastest path I found:

1. Install Ollama. It’s the baseline for local testing.
2. Pull LLaMA 3: ollama run llama3:8b. Use this for your daily chat.
3. Pull DeepSeek Coder: ollama run deepseek-coder-v2:16b. Use this when you have a bug you can’t solve.
4. Download Continue.dev for VS Code. Point it to your local Ollama instance.
5. Enable Flash Attention. If your software supports it, turn it on. It’s a free 20% speed boost on modern GPUs.

The Verdict: Which One Wins?

I will be type the straight lore to you.

Use LLaMA 3 8B if: You’re on a mid-range PC, want instant responses, and make mainly general scripting or assistant explanations. It’s the king of low latencies.Use DeepSeek Coder V2/V3 if: You really mean business. If you are working in a large repo and have to make five different files talk to each other Just Because, the model has to really understand — in this case, DeepSeek is the only way. It is slower, but it’s a lot smarter at logic. It cooks the context window when LLaMA 3 starts to be delirious.

For my daily efforts with ML/DL-based codes like SiNLP, I have my LLaMa 3 8B running out as a autocomplete (I don’t want to wait for ghost text) and my DeepSeek Coder V2 at hand if ever I need to refactor an entire module.

As for speed, that depends on your hardware. DeepSeek made a more usable, efficient product. Meta produced a great general model–but the team at DeepSeek put out a better tool for engineers. Just make sure you have the VRAM to back it up.

Don’t believe the hype about “cloud-only” AI. We are at the point where the hardware on your desk can outperform a congested API on a Friday afternoon. Pick your model, shrink it down with quantization, and get to work.