Local LLM Hardware in 2026: 3-Way GPU War [Guide]

Originally published at kunalganglani.com — read it there for inline code, hero image, and live links.

Local LLM hardware in 2026 is the set of GPUs, unified-memory systems, and workstations that let you run large language models entirely on your own machine — no API calls, no cloud bills, no data leaving your network. Qwen3, Gemma 4, and GPT-OSS 120B have blown past the VRAM tiers that existed a year ago, and the buying advice that used to be simple ("just get a 3090") now depends on a genuine three-way platform war between NVIDIA, Apple, and a surprising newcomer: Intel.

I've been running local LLM inference on everything from a Mac Studio to a dual-GPU Linux rig for the past eighteen months. This guide is what I wish existed when I started: a tiered buying guide based on actual benchmarks, real model requirements, and the specific use cases that matter to working engineers in mid-2026.

What Changed for Local LLM Hardware in 2026

Twelve months ago, the local LLM hardware conversation was simple: buy an RTX 3090 or get a Mac with lots of unified memory. That advice isn't wrong, but it's now incomplete.

Three things broke the old consensus:

1. Model sizes blew past 24GB VRAM. Qwen3 30B, Gemma 4 31B, and Qwen3 Coder Next 80B all require more than what a single 24GB card can hold at full precision. The 24GB ceiling that defined the RTX 3090 era is now a hard bottleneck for the most capable open-weight models.

2. Intel showed up. The Intel Arc B70 shipped with 32GB VRAM specifically targeting inference workloads. First benchmarks from the Hardware Corner team show it competing directly with the RTX 3090 at a consumer-accessible price. That's the first time a non-NVIDIA GPU has offered 32GB VRAM outside workstation cards.

3. Apple's M5 Max dropped real benchmarks. Reddit user cryingneko posted the first local LLM tests on the M5 Max, and the unified memory architecture (up to 128GB shared between CPU and GPU) means it can load models that would need multi-GPU NVIDIA setups. Token throughput is lower than discrete GPUs, but fitting a 70B model in memory on a laptop changes the math entirely.

So for the first time, there's no single "right" answer for local AI hardware. Your choice depends on what models you're running, how much throughput you need, and whether you value raw speed or the ability to load bigger models.

The VRAM Tiers: What Fits Where in Mid-2026

Every local LLM hardware decision starts with one question: how much VRAM do you need? The answer depends on model size, quantization level, and context window.

Here's the reality as of June 2026, based on Q4_K_M quantization (the sweet spot for quality vs. VRAM that most practitioners in the llama.cpp community have settled on):

Here's what I keep telling people: the 24GB tier is no longer the comfortable default. Gemma 4's 31B variant barely fits on 24GB at Q4, and if you want any meaningful context window (say, 16K+ tokens), you'll spill into system RAM and take a throughput hit. I've been working with these models daily. The 32GB tier is where the new comfort zone lives.

This is exactly why Intel's Arc B70 matters. It's the first card to offer 32GB VRAM at a consumer price point, directly addressing the wall that blocked running 30B+ models fully on-GPU.

NVIDIA vs Apple vs Intel: The Three-Way Platform War

Let me break down what each platform actually delivers for local LLM workloads, because the marketing claims and the real-world performance tell very different stories.

NVIDIA: Still the Throughput King

NVIDIA's CUDA ecosystem remains the most mature option for local inference. Georgi Gerganov's llama.cpp — now at 117,000 GitHub stars — runs fastest on NVIDIA hardware, and every major inference engine (vLLM, TensorRT-LLM, Ollama) is optimized for CUDA first.

The RTX 5090 with 32GB VRAM and 1,792 GB/s memory bandwidth is the new prosumer flagship. According to Hardware Corner's benchmark database, it delivers the highest tokens-per-second of any consumer GPU across all model sizes. The RTX Pro 6000 Blackwell at 96GB is the workstation answer for running 70B+ without any compromises.

But here's the thing nobody's saying about NVIDIA's lineup: the RTX 3090, a card from 2020, is still their best value recommendation on Hardware Corner's benchmark page. At 24GB VRAM and 986 GB/s bandwidth, it runs models up to 34B at Q4 fully on-GPU. You can pick one up used for a fraction of the RTX 5090's price. I've shipped multiple local agentic coding workflows on a 3090, and it handles everything up to Qwen3 30B without breaking a sweat.

A six-year-old GPU being the best value proposition in the lineup? That tells you something about how NVIDIA prices their newer cards.

Apple: The Silent Giant-Killer

The M5 Max's unified memory architecture is its actual superpower. With up to 128GB of memory shared between CPU and GPU, you can load models that would require two or three discrete GPUs on a PC. On a laptop. That draws under 30 watts.

I wrote about Apple's M5 Max making the case for local AI development when the chip launched, and the first benchmarks confirm what I expected. Token throughput runs roughly 40-60% of what an equivalent NVIDIA discrete GPU delivers, but the ability to fit a 70B model entirely in memory on a MacBook Pro is something NVIDIA simply can't match in a mobile form factor.

The tradeoff is real, though. If you're running a local LLM as a coding assistant where response latency matters, the M5 Max feels sluggish compared to an RTX 5090. I notice the difference every time I switch between my setups. But if you're doing batch processing, RAG pipelines, or running models that just don't fit in 32GB of discrete VRAM, Apple Silicon is the pragmatic choice. After running both setups side by side for months, I reach for the Mac when model size matters and the PC when throughput matters.

Apple's MLX framework continues to mature, and the llama.cpp Metal backend has gotten substantially faster in 2026. The ecosystem gap is closing, but it's not closed yet.

Intel: The Spoiler

Intel's Arc B70 is the most interesting wildcard in local LLM hardware right now. At 32GB VRAM, it slots into the gap between the RTX 3090 (24GB) and the RTX 5090 (32GB) at a price that undercuts both. The Hardware Corner benchmark team compared it directly against the RTX 3090, and while raw throughput is lower (Intel's software stack for inference is still catching up), the 32GB VRAM means it can load models the 3090 simply can't fit.

I haven't personally tested the B70 yet. I want to be honest about that. But I've been following the ROCm story long enough to know that software maturity matters more than hardware specs. Intel needs llama.cpp SYCL backend support to be rock-solid, and it's getting there but isn't at CUDA parity. If you're the kind of engineer who doesn't mind filing GitHub issues and working around rough edges, the B70 is genuinely compelling. If you need production reliability today, NVIDIA or Apple is the safer bet. Full stop.

GPT-OSS 120B on Consumer Hardware: MoE Offloading Changes Everything

A year ago, this would've gotten you laughed out of any hardware forum: you can run a 120-billion parameter model on a single RTX 3090.

OpenAI's GPT-OSS 120B uses a Mixture-of-Experts (MoE) architecture, which means only a subset of the model's parameters are active for any given token. Hardware Corner tested CPU offloading of MoE layers — inactive expert layers live in system RAM instead of VRAM — and found it viable on a 24GB card. Throughput takes a hit compared to full GPU inference, but you get a 120B-class model running locally on hardware you can buy used for under $800.

This is the local LLM equivalent of what happened when quantization first made 7B models run on laptops. It doesn't replace having enough VRAM to fit the whole model, but it dramatically lowers the barrier for experimenting with frontier-scale open models.

The practical takeaway: if you already own an RTX 3090 and have 64GB+ of system RAM, you can test GPT-OSS 120B today without buying new hardware. Pair it with llama.cpp's speculative decoding and multi-token prediction, and you can squeeze meaningful throughput out of the setup.

You no longer need a $10,000 rig to run big models. I've shipped enough features on consumer hardware to know this isn't theoretical anymore. MoE offloading made 100B+ model experimentation accessible on hardware most engineers already own.

Qwen3 and Gemma 4: The Models Driving Hardware Decisions

The two model families actually driving local LLM hardware purchases right now are Qwen3 from Alibaba and Gemma 4 from Google DeepMind. If you want to know what GPU to buy, start with what models you want to run.

Qwen3 spans the full range: 8B fits comfortably on 12GB VRAM, 14B needs 16GB, 30B targets the 24GB tier, and Qwen3 Coder Next 80B requires either multi-GPU setups or a high-VRAM workstation card. The 30B variant has become the workhorse for developers who want a coding-capable model on a single RTX 3090 or RTX 4090. I've been comparing local LLMs for daily coding and Qwen3 30B at Q4 on a 3090 is the current sweet spot for quality vs. hardware cost. It's just good enough to replace API calls for most coding tasks, and just cheap enough to justify the hardware.

Gemma 4 comes in 26B and 31B multimodal variants. Google AI Developer Relations published a fine-tuning tutorial in June 2026 showing Gemma 4 deployed locally for specialized tasks. The 26B variant runs on 24GB at Q4 with room for context. The 31B variant is tighter — you'll want 32GB VRAM for comfortable inference with any meaningful context window.

Both families have dedicated hardware requirement guides on Hardware Corner, which tells you these are the queries people are actually searching for. If you're buying hardware specifically for local LLM work in 2026, size your VRAM for Qwen3 30B or Gemma 4 31B at minimum. That means 24GB is the floor, and 32GB is my actual recommendation.

For a deeper look at how Gemma 4 compares to API-based models, I tested the 12B variant against GPT-4o Mini and Claude Haiku earlier this year.

The Cost Argument: Why Local LLM Hardware Pays for Itself

Let me talk money, because this is where engineers who are still paying for API calls need to hear something uncomfortable. Ken W. Alger, a software architect, published data in June 2026 showing that production AI implementations burn up to 30% of their cloud compute budgets on what he calls the "Prose Tax" — raw conversational data routed to LLM APIs without local pre-processing.

Let me put this in concrete terms. If your team spends $3,000/month on API calls to Claude or GPT-4, roughly $900 of that is going to queries that a local 14B model could handle: classification, summarization, simple tool calls, data extraction. A one-time investment of $800-$1,200 in an RTX 3090 pays for itself in three to four months of reduced API spend.

I've seen this pattern at multiple companies now. The strategy isn't "replace cloud AI entirely" — it's "route the cheap queries locally and save the API budget for tasks that actually need frontier intelligence." This is basically what Netflix did with their Headroom framework for cutting AI agent costs, applied at the hardware level.

The Lenovo ThinkStation P3 Tower Gen 2 with a 48GB Blackwell GPU, listed by Hardware Corner in March 2026 at near the cost of the GPU alone, is the kind of deal that makes enterprise local inference a no-brainer. A 48GB workstation for roughly the price of a standalone GPU means you can run 70B models at Q4 without CPU offloading, in a package that IT departments can actually procure through normal channels. I've seen too many engineers try to expense a bare GPU and get shot down by procurement. A Lenovo workstation with a PO number? That goes through.

Software Optimizations That Give You Free Performance

Before you buy new hardware, there are two inference optimizations that can meaningfully boost your existing setup's tokens-per-second without touching VRAM:

Speculative decoding uses a small "draft" model (say, a 1B parameter model) to predict multiple tokens ahead, then verifies them against the larger model in a single forward pass. When the draft model guesses correctly — which happens surprisingly often for common code patterns and natural language — you effectively get multiple tokens for the cost of one large-model inference. llama.cpp has native support for this, and Hardware Corner's testing shows 30-50% throughput improvements on some workloads. That's not a rounding error. That's the difference between a model feeling responsive and feeling sluggish.

Multi-token prediction (MTP) is a related technique where models trained with MTP heads can output multiple tokens per forward pass natively. Not all models support this yet, but Qwen3 and several other 2026 model families do. The throughput gain is essentially free in the sense that it requires no additional VRAM — just a model that was trained for it.

Both techniques stack with hardware upgrades. If you're running a local LLM on an RTX 3090 today, enabling speculative decoding in llama.cpp is the highest-ROI change you can make before spending money on new silicon. I covered the broader local LLM tooling ecosystem in a separate comparison if you want to dig into runtime options.

The Tiered Buying Guide: What to Buy Based on Your Use Case

Here's my opinionated recommendation for local LLM hardware in mid-2026, broken down by what you're actually trying to do:

Tier 1: Casual Experimentation ($0–$400)
You want to try local AI models, run chatbots, or mess around with small coding assistants. Any GPU with 12GB+ VRAM works. An RTX 3060 12GB is the budget champion. An older MacBook Pro with 16GB unified memory handles 7B–8B models through Ollama or LM Studio. Don't overthink this tier. You're exploring, not shipping.

Tier 2: Serious Daily Driver ($800–$1,500)
You want a local model that actually replaces API calls for coding, RAG, or agent workflows. The RTX 3090 (used, ~$800) remains the best value in all of computing. I'm not exaggerating. 24GB handles Qwen3 30B and Gemma 4 26B at Q4. Pair it with 64GB system RAM for MoE offloading flexibility. On the Apple side, a MacBook Pro with M4 Max (64GB unified memory) is the comparable option if you're already in the ecosystem.

Tier 3: Power User ($1,500–$3,500)
You run multiple models, need large context windows, or want to handle 70B models without offloading. The RTX 5090 (32GB, 1,792 GB/s) is the new benchmark here. The Intel Arc B70 (32GB) is a viable alternative if you value VRAM over peak throughput. On Apple, the M5 Max with 128GB unified memory is unmatched for loading massive models on a single machine. The Lenovo ThinkStation P3 Tower Gen 2 with 48GB Blackwell GPU is the workstation pick.

Tier 4: No Compromises ($5,000+)
You're running production AI inference locally, serving multiple users, or need 120B+ models at full speed. The RTX Pro 6000 Blackwell with 96GB VRAM and 1,790 GB/s bandwidth is the answer. Hardware Corner's benchmarks show it handling GPT-OSS 120B without any offloading. At this tier, you're building a homelab AI server that rivals cloud inference costs within months.

What About AMD?

I know someone's going to ask. The RX 7900 XTX offers 24GB VRAM at a lower price than the RTX 4090, and ROCm support has improved significantly in 2026. But the software gap is still real. llama.cpp works on ROCm, Ollama works on ROCm, but you'll hit more edge cases and compatibility issues than with CUDA. I've been through the ROCm debugging cycle enough times to know: if you're comfortable with that kind of friction and the price savings matter to you, AMD is a legitimate option for the 24GB tier. If you want things to just work on a Saturday afternoon, NVIDIA or Apple Silicon is still the path of least resistance.

I wrote a detailed ROCm vs CUDA comparison earlier this year that goes deeper on this tradeoff.

What Comes Next for Local LLM Hardware

I've been tracking hardware categories for 14+ years of building software. The local inference market is moving faster than anything I've seen. Here's where I think it goes:

The 32GB tier becomes the new default. Just as 24GB defined 2024–2025, 32GB will define 2026–2027. Both NVIDIA (RTX 5090) and Intel (Arc B70) are pushing 32GB into the consumer price range. Within a year, recommending less than 32GB for local LLM work will feel like recommending 8GB RAM for development in 2020. Technically possible. Practically painful.

MoE architectures will make VRAM ceilings less absolute. GPT-OSS 120B proved that CPU offloading of expert layers is viable. As more model families adopt MoE, the hard VRAM wall softens. This doesn't eliminate the need for VRAM — full GPU inference is always faster — but it means the minimum viable hardware for any given model size drops.

Apple's unified memory advantage widens. No discrete GPU can match 128GB of unified memory in a laptop form factor. Period. As models continue to grow and multimodal inference (vision + text + audio) demands more memory, Apple Silicon becomes increasingly compelling for developers who need to prototype with large models before deploying to GPU servers.

The three-way competition between NVIDIA, Apple, and Intel is driving VRAM up, prices down, and software ecosystems forward faster than any single vendor would on their own. If you're still running everything through API calls and paying the Prose Tax, June 2026 is a very good time to buy your first inference GPU. The hardware is ready. The models are ready. The boring answer — "just buy a used 3090 and start" — is actually the right one.

Frequently Asked Questions

How much VRAM do I need for local LLM inference in 2026?

For models up to 8B parameters (like Llama 3 8B or Qwen3 8B), 12GB VRAM is sufficient. For the most popular mid-range models in 2026 — Qwen3 30B and Gemma 4 26B/31B — you need 24–32GB VRAM at Q4 quantization. Running 70B+ models without CPU offloading requires 48GB or more.

Is the RTX 3090 still worth buying for local LLMs in 2026?

Yes. The RTX 3090 remains the best value GPU for local LLM inference in 2026, according to Hardware Corner's benchmark rankings. Its 24GB VRAM handles models up to 34B at Q4 quantization, and it can run 120B MoE models with CPU offloading. Used prices around $800 make it an unbeatable entry point for serious local inference.

Can I run a 70B parameter model on a MacBook?

With an Apple M5 Max configured with 128GB unified memory, yes. The unified memory architecture lets you load models that would require multiple discrete GPUs on a PC. Token throughput is lower than an equivalent NVIDIA setup, but the model fits entirely in memory without any CPU offloading or multi-GPU complexity.

How does Intel Arc B70 compare to NVIDIA for local LLMs?

The Intel Arc B70 offers 32GB VRAM at a consumer-accessible price, which is its main advantage — it can load models that don't fit on a 24GB RTX 3090. However, raw token throughput is lower than NVIDIA equivalents, and Intel's inference software stack (SYCL backend in llama.cpp) is still maturing. It's best for users who prioritize model capacity over speed.

What is MoE CPU offloading and why does it matter?

Mixture-of-Experts (MoE) models only activate a subset of their parameters for each token. CPU offloading moves inactive expert layers to system RAM, freeing up VRAM for the active layers. This lets you run models like GPT-OSS 120B on a single 24GB GPU — something that would otherwise require 96GB+ of VRAM. The tradeoff is slower inference, but it makes frontier-scale experimentation possible on consumer hardware.

Is cloud AI or local AI cheaper for development?

It depends on your volume. If you spend more than roughly $300/month on LLM API calls, a one-time GPU investment (RTX 3090 at ~$800) pays for itself within a few months. Local inference is especially cost-effective for high-volume, lower-complexity tasks like classification, summarization, and data extraction that don't require frontier-model intelligence.

Originally published on kunalganglani.com