Apple Silicon vs RTX 4090 for Local LLMs: A Real-World Comparison
Q4-Q5, an RTX 4090 is roughly 1.8-2.4× faster than an M3/M4 Max and noticeably cheaper to put together if you are willing to build a tower. For 30B-70B models, the picture flips: the 4090 is at or past its 24 GB ceiling, and a 64 GB or 128 GB Apple Silicon machine is the only way to run them at usable speeds without going to a workstation card. Neither machine is the universal winner - your largest target model picks the platform.
What we are actually comparing
For this article, "Apple Silicon" means the M3 Max and M4 Max chips with their highest-end GPU configurations (40-core GPU on M3 Max, similar on M4 Max), running in a MacBook Pro or Mac Studio. We will also reference the M-Ultra (M2 Ultra still widely deployed; M3/M4 Ultra in Mac Studio) when it is decisive. "RTX 4090" means the desktop card with 24 GB of GDDR6X, paired with a typical mid-range gaming CPU and 32-64 GB of system RAM.
Both machines run the same LLMs through the same engines - llama.cpp on Apple Silicon via Metal, and through CUDA on the 4090. Ollama and LM Studio wrap both. We are not comparing fine-tuning or training in this article (NVIDIA still dominates that conversation in 2026); we are talking purely about inference for chat, coding, and agent loops.
The headline numbers
Approximate sustained tokens-per-second on common workloads, drawn from public llama.cpp benchmarks and our own measurements at typical default settings (Q4_K_M weights, F16 KV cache, 4k context unless noted). Ranges reflect variance between builds, OS versions, and inference engines.
| Model | Quant | RTX 4090 (24 GB) | M3/M4 Max 64-128 GB | M2/M3 Ultra 128-192 GB |
|---|---|---|---|---|
| Llama 3.1 8B | Q4_K_M | 150-180 tok/s | 55-70 tok/s | 80-100 tok/s |
| Mistral 7B | Q4_K_M | 165-195 tok/s | 60-75 tok/s | 85-105 tok/s |
| Qwen 14B | Q4_K_M | 85-105 tok/s | 32-42 tok/s | 48-60 tok/s |
| Mixtral 8x7B (MoE) | Q4_K_M | spills to RAM | 22-30 tok/s | 34-44 tok/s |
| Qwen 32B | Q4_K_M | ~20-28 tok/s (tight) | 14-20 tok/s | 22-28 tok/s |
| Llama 3.1 70B | Q4_K_M | 3-6 tok/s (offloads) | 9-12 tok/s | 14-18 tok/s |
| Llama 3.1 70B | Q5_K_M | does not fit | 7-10 tok/s | 11-15 tok/s |
Read the table in two slices. Above the dividing line at 14B, the 4090 wins by 1.8-2.4× on throughput; both platforms are well above human reading speed. At 32B and beyond, the 4090 either gets uncomfortable (Qwen 32B sits right at its limit) or simply cannot fit (Llama 3.1 70B at Q5, Mixtral 8x7B at Q4), and the Mac surfaces as the only single-machine option.
Why the speed gap exists for small models
As we explained in VRAM vs Unified Memory, local LLM inference is dominated by memory bandwidth. The RTX 4090 has roughly 1008 GB/s of GDDR6X bandwidth; the M3/M4 Max sits in the 410-546 GB/s range. That is roughly a 2× advantage on paper, and it shows up almost directly as a 2× throughput advantage on models that fit in both. The 4090 also benefits from a more mature inference stack: CUDA kernels for quantized matmul have had years more optimization than Metal kernels, though the gap has narrowed substantially in 2025-2026 thanks to Apple's MLX framework and improved Metal backends in llama.cpp.
The Ultra chips close most of the bandwidth gap (800-820 GB/s) and accordingly sit at roughly 0.7-0.85× the 4090's throughput on small models. They are also several times more expensive, which is the obvious caveat.
Why Apple Silicon wins for big models
One word: capacity. A 4090 has 24 GB of VRAM, full stop. A Llama 3.1 70B at Q4_K_M needs ~42 GB just for weights, plus a few more for KV cache. The model literally cannot live on the 4090 alone - inference engines have to offload layers to system RAM, where they are read across PCIe at a fraction of GDDR6X bandwidth. Throughput collapses from "fast chat" to "slow paragraph generator," typically 3-6 tok/s in our experience.
On a 64 GB or 128 GB unified-memory Mac, the 70B simply fits. Throughput is bandwidth-limited (9-18 tok/s depending on chip and model size), but it is consistent and feels like a real assistant. For a single-machine, no-fuss setup that needs to run a 70B-class model, in May 2026 there is no real alternative on the consumer side.
Q4, where the 4090 starts to feel cramped.
The other dimensions that matter
Power draw and heat
An RTX 4090 under inference load draws 320-380W on the card alone, with the full system pulling 450-500W from the wall. An M3/M4 Max under the same workload draws roughly 40-60W total system power. For a desktop in a basement office, this is a "whatever, electricity is cheap" discussion. For a laptop on battery or a machine that has to share an outlet with other equipment, it is the entire conversation. The Mac will also be silent or near-silent during inference; the 4090 will not.
Form factor
The Mac is portable. A MacBook Pro M4 Max with 64 GB of unified memory will run a 32B model on a flight, in a coffee shop, on battery, with no external dependencies. There is simply no equivalent on the NVIDIA side - the 4090 is a desktop card, full stop. NVIDIA's mobile 4090 (the laptop part) is a substantially cut-down chip with only 16 GB of VRAM and is not in the same conversation for serious local LLM work.
Software ecosystem
CUDA on NVIDIA remains the broader, deeper ecosystem in 2026. If you want to fine-tune, run image-generation models, do speech, or string together a multi-model pipeline, the path of least resistance is still NVIDIA. Apple's MLX has matured remarkably fast and is excellent for inference, with first-party support for most modern open architectures - but the long tail of research code (PyTorch + CUDA-specific kernels, custom triton ops, vLLM-class servers) still assumes NVIDIA. If your local LLM use stays close to "load a GGUF, chat with it, occasionally run an embedding model," the gap is small. If it expands beyond that, NVIDIA is the safer bet.
Sustained throughput
A well-cooled 4090 (a tower with adequate airflow) will hold its peak throughput indefinitely. A MacBook Pro under a long agent run will start to throttle GPU clocks after 10-15 minutes of sustained load - we have measured 10-20% throughput drops on long-running workloads. The Mac Studio (which has more thermal headroom) holds throughput much better; if you plan to leave a model running overnight, the Studio is a meaningfully different machine from the laptop with the same chip.
Long-context behavior
Long context magnifies the capacity advantage. A 14B at 32k context with full F16 KV cache pushes the 4090 right to its limit; a 32B at 32k overflows. On a 64 GB+ Mac, you can keep raising the context until the model itself loses the plot. For RAG, code editing on large repositories, or long agent traces, the Mac is more comfortable.
Cost, with full systems
Approximate complete-system pricing in mid-2026 (USD, used or new street prices):
| Configuration | Approx. cost | Best for |
|---|---|---|
| RTX 3090 24 GB tower (used GPU + new system) | $1500-2000 | Chat with up to 32B at Q4; budget option for the 24 GB tier. |
| RTX 4090 24 GB tower (new) | $2500-3300 | Best-in-class throughput up to 32B; multi-modal pipelines. |
| Mac Mini M4 Pro 48 GB | $1700-2200 | Quiet small-form-factor for chat up to ~32B. |
| MacBook Pro M4 Max 64 GB | $3700-4500 | Portable; runs 70B at Q4 on the road. |
| Mac Studio M-Ultra 128 GB | $5500-6500 | Best Apple-side single-box experience; comfortable 70B with long context. |
| Mac Studio M-Ultra 192 GB | $7500-8500 | 120B+ frontier-scale open models; quiet 24/7 inference. |
| Dual RTX 3090 / 4090 workstation | $3500-7500 | NVIDIA path to 48 GB combined VRAM with more setup work. |
Concrete recommendations by user profile
"I want to chat with a 7B-14B model and the speed should feel snappy"
RTX 4090 tower. Or RTX 4070/4080 if you do not need 24 GB. Cheaper than a comparable Mac, ~2× faster on the workloads you will actually run.
"I want to run a 70B model at home"
Apple Silicon, 64 GB minimum, ideally 128 GB. MacBook Pro M4 Max if portability matters, Mac Studio Ultra if it does not. Multi-GPU NVIDIA workstations work but are noisier, larger, and more expensive for the same effective capacity.
"I want to fine-tune small models occasionally and also chat"
RTX 4090 tower. CUDA ecosystem advantages outweigh capacity considerations until you cross 24 GB.
"I want one machine I can carry that does everything reasonably"
MacBook Pro M4 Max 64 GB. No NVIDIA alternative gives you 64 GB of GPU-addressable memory in a laptop. You give up some throughput on small models in exchange for actually being able to run big ones on the road.
"I want to run agent loops 24/7 on a quiet machine"
Mac Studio Ultra for low noise and steady throughput. The 4090 will be faster per token but you will hear it and feel its waste heat. If noise does not matter and you have good cooling, a 4090 with proper case airflow is the throughput king for sub-32B work.
What we did not test
A few things worth flagging honestly:
- The RTX 5090. The 32 GB ceiling and ~1.8 TB/s bandwidth meaningfully change the picture - a 5090 fits comfortably more 32B models and starts to make 70B at
IQ3_XXSviable. We will publish a 5090-specific update once we have run our full benchmark suite on it. - Speculative decoding. A small "draft" model paired with a large target model can roughly double effective throughput on both platforms. We left it out of the headline numbers because it is workload-dependent.
- MLX vs llama.cpp on Apple Silicon. MLX is sometimes 10-25% faster than llama.cpp's Metal backend on Apple Silicon. The numbers in the table above use llama.cpp for parity with the NVIDIA side; on MLX, the Mac figures move up but do not change the qualitative conclusions.
The one-sentence summary
The RTX 4090 is the right machine if your daily driver is a small-to-medium model and you care about raw speed; Apple Silicon is the right machine if you want to actually run the largest open models without buying a workstation. Use our home-page checker with the specific model and quantization you have in mind - the answer for "your" exact configuration is one click away.