Strix Halo 128GB vs RTX 5090 for Local AI

1. 30-second verdict

This is a unified memory versus discrete VRAM comparison, not a like-for-like GPU duel. AMD Strix Halo (Ryzen AI Max+ 395) wins addressable memory: 128 GB of unified LPDDR5X shared by the CPU, the Radeon 8060S GPU (40 compute units), and the NPU — enough for our calculated 48 GB minimum for Llama 3.1 70B at Q4_K_M / 4k context, with room to spare. The RTX 5090 wins bandwidth and sourced throughput: 32 GB GDDR7 discrete VRAM at 1792 GB/s versus Strix Halo's 256 GB/s, with 14 non-quarantined benchmark rows (llama.cpp Llama-3.1-8B Q4_K_M at 215 tok/s, UL Procyon, ComfyUI). Strix Halo has zero benchmark rows in our database — no Strix Halo tok/s is claimed on this page. Launch MSRP: $2,199 for the GMKtec EVO-X2 mini PC (or $3,999 for the AMD Halo Developer Platform) versus $1,999 for the 5090 card. Street and used prices are not yet measured.

2. Memory architecture

These two products do not share a memory architecture, and the numbers are not interchangeable.

• Strix Halo (Ryzen AI Max+ 395): unified memory. One 128 GB LPDDR5X-8000 pool is shared by the CPU (16 cores), the Radeon 8060S GPU (40 compute units), and the NPU (50 TOPS). Our specifications table records this as memory_gb = 128, memory_type = LPDDR5X 8000, memory_bandwidth_gbps = 256, memory_bus_width = 256-bit, and memory_upgradeable = No (soldered LPDDR5X). There is no separate VRAM pool.
• RTX 5090: discrete VRAM. 32 GB of GDDR7 on a 512-bit bus at 1792 GB/s, recorded as memory_architecture = discrete_vram, gpu_addressable_gb = 32, physical_memory_gb = 32 in the gpus table. Addressable GPU memory equals physical VRAM.

Capacity and bandwidth point in opposite directions by a wide margin: Strix Halo holds four times the memory at roughly one-seventh of the bandwidth. A 70B-class model at Q4 needs about 48 GB of calculated minimum — it fits the 128 GB unified pool, it does not fit 32 GB of discrete VRAM. An 8B model fits both, and then the 1792 GB/s bandwidth column decides speed. Because the Strix Halo pool is shared, OS and CPU overhead also live in that 128 GB; community builds on this platform often split the pool into a large GPU region and a smaller host region, but our database records no measured split configuration, so this page claims none.

3. Model-fit table

Fit labels use our ai_models calculated min/recommended configs against each product's memory record — 128 GB unified for Strix Halo, 32 GB discrete VRAM for the 5090. They are capacity checks, not speed claims.

For Strix Halo, "fits" means the model fits inside the 128 GB unified pool alongside OS and CPU overhead — a capacity statement only. Strix Halo decode speed for these models is not yet measured in our benchmarks table.

4. Direct benchmark table

Only non-quarantined benchmarks rows. Strix Halo products have zero rows — every Strix Halo cell below stays not-yet-measured. Estimate means is_estimate=1.

The absence of Strix Halo rows is a data gap, not a verdict. ROCm, Vulkan, and DirectML are all recorded as supported frameworks on Strix Halo, but no measured run is in our benchmarks table, so this page claims no Strix Halo tok/s. The 256 GB/s unified bandwidth is the key structural constraint for large-model decode: it is roughly one-seventh of the 5090's 1792 GB/s, so even when a 70B model fits the 128 GB pool, prompt processing and decode are bandwidth-bound on this platform.

5. Runtime compatibility

The two platforms do not share a runtime. The 5090 is an NVIDIA CUDA discrete GPU: sourced stacks in our rows are llama.cpp CUDA, vLLM (estimate rows), Diffusers (estimate), ComfyUI, and UL Procyon TensorRT. Strix Halo records ROCm, Vulkan, and DirectML as supported AI frameworks, with Windows 11 and Linux OS support. CUDA does not apply to Strix Halo. Cross-platform runtimes (llama.cpp itself, and Vulkan-based stacks) exist on both, but no same-configuration pair is measured here. Fine-tune and training stacks on Strix Halo are not yet measured on this page.

6. Quantization compatibility

Sourced 5090 rows use Q4_K_M and Q4_K_XL on 8B models, UL Procyon default (unspecified quant) TensorRT text scores, and FP16 / INT8 image rows, plus an estimate BF16 FLUX pair. GGUF quantization on Strix Halo via ROCm/Vulkan/llama.cpp is not yet measured in our rows. Capacity still follows the model-fit table: the 128 GB unified pool versus 32 GB discrete VRAM changes which quant + context combination fits, not which runtime exists.

7. Max practical context examples

Sourced 5090 llama.cpp rows use 4k context (Llama-3.1-8B) and 16K context (Qwen3-8B). Llama 3.1 8B native context in our model record is 131072 tokens; whether either platform holds that window at a given quant is not yet measured here. For Llama 3.1 70B the calculated min is 48 GB at 4k Q4_K_M — that fits Strix Halo's 128 GB unified pool and does not fit the 5090's 32 GB, so long-context 70B is a Strix Halo-side capacity argument, with Strix Halo decode speed still not-yet-measured.

8. Power

Specifications record 120 W for the Strix Halo platform (Ryzen AI Max+ 395, SoC TDP) and 575 W TDP for the RTX 5090 (manufacturer datasheet, both). The Strix Halo figure is a platform/SoC power class for the whole mini PC, the 5090 figure is board power for the card alone — they are not the same measurement class, and a 5090 also needs a host system with its own power budget. Energy cost per million tokens is not yet measured (no watt-hour rows).

9. Current price

Price class is launch_msrp only. Products table: GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB) $2,199, AMD Ryzen AI Halo Developer Platform (Max+ 395) $3,999, RTX 5090 $1,999. Strix Halo prices checked 2026-07-21, 5090 checked 2026-09-02. street_price_usd is NULL on all three. No product_offers rows. The EVO-X2 is a complete mini PC; the 5090 is a card that still needs a host PC, PSU, and case. Check live listings rather than treating MSRP as street:

• GMKtec EVO-X2 Strix Halo 128 GB current listing (affiliate)
• RTX 5090 current listing (affiliate)
• EVO-X2 product page · RTX 5090 product page

10. Used price

Not yet measured. No used-condition offer rows exist for either product. A used 5090 or a used Strix Halo mini PC can be the right buy, but this page will not invent a median.

11. Performance per current dollar

Not computed. Street price is unknown on both, Strix Halo has no benchmark rows to rank, and mixing launch MSRP with 5090-only tok/s would invent a value ranking the database does not store. Use the sourced table plus the price you actually pay.

12. Multi-GPU considerations

The desktop RTX 5090 records nvlink_support = 0 in the gpus table; two 5090s are 2×32 GB, not 64 GB of one pool, unless the runtime shards (tensor parallel / pipeline / expert offload). Strix Halo is a system-on-package with a single 128 GB unified pool and no discrete PCIe GPU slot in our product records — there is no second-card path. The EVO-X2 and the Halo Developer Platform do expose OCuLink, but no measured eGPU configuration exists in our database, so no eGPU performance is claimed. Dual-5090 tok/s and any Strix Halo eGPU setup are not yet measured. Related system pages: workstations, multi-GPU guide.

13. System requirements

RTX 5090: PCIe 5.0 x16, 575 W TDP, needs a case and PSU rated for a flagship card plus a host CPU and system RAM. Strix Halo (EVO-X2): integrated Ryzen AI Max+ 395 package in a mini PC at a 120 W platform profile — no PCIe GPU slot, no board-power planning; expansion is external (OCuLink), and memory is soldered LPDDR5X with no upgrade path. CPU/RAM pairing for a 5090 build is not yet measured on this page; see workstations for configured systems.

14. Who should buy Strix Halo

• 70B-class Q4 work (calculated min 48 GB) and even GPT-OSS 120B-class work (calculated min 96 GB) that cannot fit in 32 GB of discrete VRAM — the 128 GB unified pool is the reason to buy this platform.
• Buyers who want one quiet, low-power (120 W platform profile) desk-side box with no separate GPU budget, and accept that Strix Halo tok/s is not yet measured in our tables.
• Anyone who needs the ROCm / Vulkan / DirectML stack on Windows 11 or Linux rather than CUDA.
• Not a substitute for the 5090 when the model fits in 32 GB and bandwidth matters (1792 GB/s vs 256 GB/s).

15. Who should buy the RTX 5090

• 8B–32B Q4 work that fits in 32 GB, where the sourced llama.cpp pair (215 tok/s Llama-3.1-8B Q4_K_M) and UL Procyon rows matter.
• CUDA-native stacks: vLLM, TensorRT, Diffusers, ComfyUI — all present in our benchmark rows.
• Buyers with a $1,999 launch-MSRP budget for a card that fits a standard PCIe 5.0 x16 workstation, rather than a fixed-configuration mini PC.
• Not a substitute for Strix Halo when the calculated minimum is 48 GB or more (Llama 3.1 70B Q4_K_M at 4k, GPT-OSS 120B at 96 GB).

16. Evidence / source table

17. Data freshness date

21 September 2026. Strix Halo product specs and prices last verified 2026-07-21 (price_checked_at); RTX 5090 specs and prices last verified 2026-09-02 (price_checked_at, gpus.last_verified_at). Newest sourced benchmark date on this pair: llama.cpp 8B on 2026-05-22 (5090 only). UL Procyon lab date: 2025-01-29. This page does not invent Strix Halo tok/s, street prices, or unsourced comparisons.

Frequently asked questions

Is Strix Halo faster than the RTX 5090 for AI?

We cannot say from our database. The RTX 5090 has 14 non-quarantined benchmark rows (llama.cpp, UL Procyon, ComfyUI); Strix Halo products have zero. What the records do show: Strix Halo holds 128 GB of unified LPDDR5X at 256 GB/s, while the 5090 holds 32 GB of discrete GDDR7 at 1792 GB/s. Capacity favors Strix Halo for large models; bandwidth and sourced throughput favor the 5090 for models that fit in 32 GB.

Can Strix Halo run Llama 3.1 70B?

It fits, yes — our model-fit record lists a calculated minimum of 48 GB at Q4_K_M / 4k context, and Strix Halo has a 128 GB unified pool. Decode speed on Strix Halo is not yet measured in our benchmarks table, and the 256 GB/s unified bandwidth is roughly one-seventh of the 5090’s 1792 GB/s, so expect capacity to be the win, not speed.

Can the RTX 5090 run Llama 3.1 70B?

Not as a single card. The calculated minimum is 48 GB at Q4_K_M / 4k context and the 5090 has 32 GB of discrete VRAM. A sourced estimate row (is_estimate=1) lists Llama-3-70B Q4 at 42 tok/s on the 5090 with a tight fit — treat that as an estimate, not a measured same-lab result.

Is 128 GB of unified memory the same as 128 GB of VRAM?

No. Strix Halo uses a unified memory architecture: one 128 GB LPDDR5X pool shared by the CPU, the Radeon 8060S GPU, and the NPU. Our specifications table records memory_gb = 128, memory_type = LPDDR5X 8000, and memory_bandwidth_gbps = 256. The RTX 5090 uses discrete VRAM: 32 GB of GDDR7 on a 512-bit bus at 1792 GB/s. Do not convert one figure into the other.

Which is cheaper, Strix Halo or the RTX 5090?

The GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB) is $2,199 and the RTX 5090 is $1,999 at launch MSRP — but the EVO-X2 is a complete mini PC and the 5090 is a card that still needs a host system. The AMD Ryzen AI Halo Developer Platform is $3,999. Street prices are not yet measured (street_price_usd is NULL).

Can you put an RTX 5090 in a Strix Halo mini PC?

No. Strix Halo products in our records have no discrete PCIe GPU slot — the Radeon 8060S is integrated. The EVO-X2 and the Halo Developer Platform do expose OCuLink, but we have no measured eGPU configuration rows, so this page claims no eGPU performance.

Related: best GPU for local LLMs, RTX 5090 vs RTX 4090, RTX 5090 vs RTX 5080, RTX 5090 vs RX 7900 XTX, M3 Ultra 96GB vs RTX 5090, AI mini PCs, GPU index, Can it run?, GPU comparison tool.