VRAM vs Unified Memory: When Each Wins for Local AI in 2026

By the RunLocalModel editorial team · Published April 22, 2026 · ~12 minute read

If you only read one paragraph Dedicated VRAM wins on speed per gigabyte; Unified Memory wins on gigabytes per dollar. If your target model fits comfortably in 24 GB or less, a discrete GPU will run it 1.5-3× faster than a similarly priced Apple Silicon machine. If you want to run a 70B model at all - or a 32B with a long context - Unified Memory is the cheapest and quietest way to get there in 2026. Most people overweight raw tokens-per-second and underweight "does it actually fit."

The two memory architectures, in one paragraph each

Dedicated VRAM is what a discrete graphics card has. It is GDDR6, GDDR6X, or HBM, soldered to the GPU board, with its own controller. The CPU cannot use it directly - data has to be copied across PCIe to and from system RAM. The big advantage is bandwidth: a single RTX 4090 has roughly 1008 GB/s of memory bandwidth, more than any consumer CPU comes near. The big disadvantage is capacity: 24 GB is the consumer ceiling in 2026, and that ceiling is a hard wall for LLM inference.

Unified Memory is what Apple Silicon (and a growing number of AI-focused PC SoCs) ships. The same physical LPDDR or LPDDR5X chips are visible to both the CPU and the GPU at the same address. There is no copy step. Bandwidth depends on the chip: the M3/M4 Max line sits in the 400-540 GB/s range, the Ultras in the 800-820 GB/s range. Capacity goes far higher than discrete VRAM - 64 GB, 96 GB, 128 GB, and (on the Ultra and Studio configurations) 192 GB are all shipping in 2026.

Why this matters for LLM inference specifically

Local LLM inference is, almost entirely, a memory bandwidth problem. Generating each new token requires reading the full set of model weights from memory at least once. Compute is rarely the bottleneck on consumer hardware - the GPU sits there waiting for the next chunk of weights to arrive. This is why a 4090 with twice the bandwidth of a 4080 generates tokens roughly twice as fast on the same model.

But there is a second, harder constraint: the model and its KV cache have to fit in the memory the GPU can address directly. The moment any portion of the model has to live in a slower tier - system RAM, swap, an SSD - generation speed collapses by an order of magnitude or worse. We call this the memory wall. On a discrete GPU, the wall is at 24 GB. On a 128 GB unified-memory Mac, the wall is somewhere around 100 GB (after leaving headroom for the OS).

So the practical question is not "which is faster?" - it is "which wall do you hit first for the model you actually want to run?"

Bandwidth, by the numbers

A reference table for the chips most local-LLM users in 2026 are choosing between. Bandwidths are vendor-published peak numbers; sustained throughput during inference is typically 65-80% of these on NVIDIA, 70-85% on Apple Silicon.

Chip	Memory type	Capacity	Peak bandwidth
RTX 3060 12GB	GDDR6	12 GB	360 GB/s
RTX 4070	GDDR6X	12 GB	504 GB/s
RTX 4080	GDDR6X	16 GB	717 GB/s
RTX 3090	GDDR6X	24 GB	936 GB/s
RTX 4090	GDDR6X	24 GB	1008 GB/s
RTX 5090 (early 2025)	GDDR7	32 GB	~1792 GB/s
M3 / M4 Max	Unified LPDDR5X	up to 128 GB	~410-546 GB/s
M2 Ultra	Unified LPDDR5	up to 192 GB	800 GB/s
M3 / M4 Ultra	Unified LPDDR5X	up to 192 GB	~819 GB/s

Two observations. First, an RTX 4090 has roughly 2× the per-second bandwidth of an M3/M4 Max. For a model that fits in 24 GB, the 4090 will roughly double the throughput. Second, an M-Ultra closes most of that gap and gives you 4-8× the capacity. The Ultras are a different price tier, but they exist for a reason.

Capacity, in practice

You do not get to use 100% of your memory for the model. There are three things competing for it:

Model weights, sized by parameter count and quantization. A 7B at Q4_K_M is ~4.4 GB; a 70B at Q4_K_M is ~40 GB; a 70B at Q5_K_M is ~48 GB.
KV cache, sized by context length, model dimensions, and KV cache precision. For a 7B at 8k context the KV cache is around 1 GB; for a 70B at 16k context with q8_0 KV it is closer to 5 GB.
OS and runtime overhead. On a discrete GPU this is small (under 1 GB). On a unified-memory Mac, you should leave 4-6 GB for macOS, the browser, and any other apps.

Plug those numbers into our home-page checker for your exact configuration; the methodology is on the How It Works page. The point here is just that "24 GB VRAM" is really "20 GB usable for the model and KV cache," and "128 GB unified" is really "100-110 GB usable."

Concrete head-to-head: who wins which workload

1. Chat with an 8B model (the most common workload)

A discrete GPU wins, and it is not close. An RTX 4070 (12 GB, 504 GB/s) generates Llama 3.1 8B at Q4_K_M at roughly 90-110 tok/s in our reading of public benchmarks. An M3 Max with the 40-core GPU on the same model lands around 50-65 tok/s. Both are well above human reading speed, but the GPU finishes a long answer in noticeably less wall-clock time.

2. Coding assistant on a 14B model with long context

Tighter race. A 14B at Q5_K_M with a 16k context fits in 16 GB but with no headroom; on a 24 GB card it is comfortable. Throughput is somewhere around 50-65 tok/s on a 4090 and 30-40 tok/s on an M3/M4 Max. The Mac wins on quietness and power draw, the 4090 wins on responsiveness for long completions.

3. Running a 70B at home, at all

Unified Memory wins outright. A 70B at Q4_K_M needs ~42 GB of weights plus ~3-5 GB of KV cache - you cannot fit it on any single consumer GPU short of an RTX 5090 with aggressive KV quantization. On a 64 GB M3/M4 Max it fits and runs at roughly 9-12 tok/s; on a 128 GB Ultra it has plenty of room and runs faster (14-18 tok/s) because the bandwidth is higher. To match this on NVIDIA you need either dual 3090s/4090s with NVLink-style splitting or a workstation card.

4. Long-context analysis (32k+ tokens)

Unified Memory wins again, for the same reason: the KV cache at long context is huge, and the discrete GPU runs out of room first. A 32B at 32k context with full F16 KV pushes well past 24 GB total memory; on a 64-128 GB Mac you can keep raising the context until the model itself starts to lose track of what is going on.

The under-discussed factor: sustained workloads. A discrete GPU with adequate cooling will hold its peak throughput essentially indefinitely. Apple Silicon machines, especially fanless ones (MacBook Air) and even fan-equipped MacBook Pros under prolonged load, will reduce GPU clocks to manage thermals - dropping inference throughput by 10-25% after the first 10-15 minutes of heavy use. If you plan to leave a model running an agent loop overnight, this matters.

Cost, honestly

Comparing prices fairly is hard because you are comparing a component to a complete computer. Approximate ranges in mid-2026:

RTX 4070 12GB build (used for ~14B Q4 chat): GPU $550 + a $700-900 host system = ~$1300-1500.
RTX 4090 24GB build (used for ~32B Q4 chat): GPU $1500-1800 + a $1000-1500 host system = ~$2500-3300.
Mac Mini M4 / M4 Pro 32-48 GB: $1500-2400 complete.
MacBook Pro M4 Max 64 GB (for occasional 70B work and full chat range): ~$3700-4500.
Mac Studio M-Ultra 128-192 GB (for serious 70B use): $5000-7500.

Per dollar, on a model that fits in 12-24 GB, the discrete-GPU build wins on tokens-per-second. Per dollar, on a model that does not fit at all on the discrete GPU, the Mac is the only option without going to multi-GPU workstations. If you are spending under $2000 and want to run 7B-14B models well, build a tower around an RTX 4070/4080 or 3090. If you are spending over $3000 and want flexibility up to 70B, look hard at Apple Silicon.

The five-question shortcut

What is the largest model you actually want to run? If under 14B, lean discrete GPU. If 30B-70B, lean Unified Memory.
How long will the model be running per session? Hours of sustained use favor discrete GPU thermals.
Do you care about ambient noise? A fanless or quiet Mac is a different living-room experience from a 350W tower.
Will you also want to fine-tune or train? CUDA on NVIDIA still has the deeper ecosystem in 2026; MLX has closed a lot of the inference gap but training is younger.
Do you already own one of these machines? The right answer is almost always "use what you have until it becomes the bottleneck."

What is changing in 2026

Two trends to watch. First, NVIDIA's RTX 5090 brought the consumer ceiling to 32 GB at almost 1.8 TB/s of bandwidth, which makes more 32B-class models viable on a single discrete card. Second, both Apple and the upcoming generation of AI-focused PC SoCs (AMD Strix Halo, Intel's Panther Lake, Qualcomm's Snapdragon X2 series) are pushing unified memory bandwidth and capacity hard. The line between "GPU computer" and "AI computer" is blurring, and unified memory is winning the architectural argument for the long tail of users who care about capacity more than peak throughput.

For now, our recommendation is the same one we give in every email: let the model you want to run pick the hardware, not the other way around. Use the home-page checker to see exactly where the wall is for your shortlist before you spend a dollar.