Llama 4 local model profile
Evidence snapshot:
Llama 4 is a local model family available through Ollama. The published local profile lists Scout / Maverick variants, 1M-10M context, and a typical 67-245GB download range; effective speed and capacity depend on hardware and quantization.
Llama 4 is a local model family available through Ollama. It is intended for large-hardware multimodal work and very long local context, with Scout / Maverick variants and a typical 67-245GB download range. Effective context, speed, and quality depend on the selected quantization and hardware.
Llama 4 runs on your own hardware through Ollama in Kendr Desktop, so the trade it asks you to make is machine capacity against privacy and per-token cost, not price per million.
Llama 4 is documented as a local Ollama family. This page does not claim a Kendr-hosted API route, hosted availability, a Kendr markup, or a routing receipt.
Llama 4 accepts text and image and returns text. The snapshot records text, image, and tools as capabilities or interfaces.
$0 token fee. Hardware, storage, memory, electricity, and operational costs remain user-provided.
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Local model family
- Provider or publisher
- Open-weight / Ollama
- Context window
- 1M-10M
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- Not disclosed
When to pick Llama 4
Llama 4 runs on your own hardware through Ollama in Kendr Desktop, so the trade it asks you to make is machine capacity against privacy and per-token cost, not price per million.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when the data cannot leave your machine. This family runs locally through Ollama in Kendr Desktop, at a typical download of 67-245GB. There is no token fee and no cloud round trip, and the cost is the hardware you run it on.
- Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
Context, modalities, and identity
Llama 4 accepts text and image and returns text. The snapshot records text, image, and tools as capabilities or interfaces.
- Provider or publisher
- Open-weight / Ollama
- Context window
- 1M-10M
- Maximum output
- No verified numeric limit
- Input modalities
- text and image
- Output modalities
- text
- Knowledge cutoff
- Not disclosed
Ollama variants and hardware boundary
The documented Ollama family uses llama4. It is suited to Large-hardware multimodal work and very long local context. Actual throughput and maximum usable context vary with the selected variant, quantization, runtime, RAM, and VRAM.
- Ollama identifier
- llama4
- Variants
- Scout / Maverick
- Typical download
- 67-245GB
Model overview
Llama 4 is a local model family available through Ollama. It is intended for large-hardware multimodal work and very long local context, with Scout / Maverick variants and a typical 67-245GB download range. Effective context, speed, and quality depend on the selected quantization and hardware.
- Reference model ID
- Not separately documented
- Canonical version
- Not separately documented
- Hugging Face ID
- Not separately documented
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- No verified parameter list
- text
- image
- tools
Local operating cost
$0 token fee. Hardware, storage, memory, electricity, and operational costs remain user-provided.
Local inference is not a zero-cost operation: the user supplies compute, memory, storage, electricity, and maintenance.
- Price date
- 2026-09-06
- Input
- $0 per 1M tokens
- Cached input
- No verified rate
- Output
- $0 per 1M tokens
Benchmark evidence and limitations
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
Local performance varies by exact variant, quantization, hardware, runtime, and settings.
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use Llama 4?
Llama 4 runs on your own hardware through Ollama in Kendr Desktop, so the trade it asks you to make is machine capacity against privacy and per-token cost, not price per million. Reach for it when the data cannot leave your machine: This family runs locally through Ollama in Kendr Desktop, at a typical download of 67-245GB. There is no token fee and no cloud round trip, and the cost is the hardware you run it on. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
What is Llama 4?
Llama 4 is a local model family available through Ollama. The published local profile lists Scout / Maverick variants, 1M-10M context, and a typical 67-245GB download range; effective speed and capacity depend on hardware and quantization.
What context window does Llama 4 have?
The dated profile lists 1M-10M of context. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Llama 4?
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated. Local performance varies by exact variant, quantization, hardware, runtime, and settings.
Can Llama 4 run locally?
Llama 4 is documented here as an Ollama family with the identifier llama4. Hardware, quantization, context settings, and the selected variant determine practical speed and memory use.
Sources and evidence dates
- Llama 4 on Ollama (primary, checked 2026-09-06)